modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 18:26:50
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 18:26:48
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
Anon1216/kdllora-v1.5
|
Anon1216
| 2023-02-23T06:49:27Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-23T06:44:47Z |
---
license: creativeml-openrail-m
---
This is korean doll likeness lora from civitai. I'm not the creator, credits go to https://civitai.com/user/Kbr
|
nandakishormpai/t5-small-github-repo-tag-generation
|
nandakishormpai
| 2023-02-23T06:27:17Z | 38 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"documentation_tag",
"tag_generation",
"github",
"github_tag",
"tagging",
"github_repo",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-02-22T16:51:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
- documentation_tag
- tag_generation
- github
- github_tag
- tagging
- github_repo
- summarization
metrics:
- rouge
model-index:
- name: t5-small-github-repo-tag-generation
results: []
widget:
- text: "susya plant disease detector ml powered app to assist farmers in crop disease detection and alerts product walkthrough download product apk here machine learning python notebook solutions system to detect the problem when it arises and warn the farmers disease detection using machine learning model enabled through android app which uses flask api solution to overcome the problem once it arises remedy is suggested for the disease detected by the app using ml model solution that will ensure that the problem will never occur in the future again pdf report is generated on the disease predicted along with user information pdf can be used as a document to be submitted in nearby krishibhavan thereby seeking help easily method that will reduce the impact of the dilemma to a significant level disease detected news can be sent to other users as a notification which contatins userplant and disease this will help other farmers take up precautions thereby reducing the impact of the dilemma to a significant level considering a region machine learning model multiclass image classifier built on pytorch framework using cnn architecture currently project detects 17 states of disease in 4 plants aiming kerala state namely cherry pepper potato and tomato framework pytorch architecture convolutional neural networks validation accuracy 777 how to train upload the python notebook to google colab and run each cell for training the model i have included a demo dataset to configure quickly you can use this kaggle dataset which is the original one with huge amount of pictures how it works the input image dataset is converted to tensor and is passed through a cnn model returning an output value corresponding to the plant disease input image tensor is passed through four convolutional layers and then flattened and inputted to fully connected layers api api is built using flask framework and hosted in render the api provides two functionalities they are plant disease detection accepts a post request with an image in the form of base64 string and returns plant disease and remedy notification accepts a post request with plant user and disease which is then pushed as a notification to other users to warn them regarding a probable outbreak of disease how to use api has been built on this classifier url user has to send a post request to the given api with base64 string of the image to be input python import requests url imgdata base64 string of image r requestsposturljson imageimgdata printrtextstrip outputpython diseaseseptoria leaf spotplanttomatoremedyremove infected leaves immediatelyfungonil and daconil app download product apk here to run app shell cd app flutter run to build app shell cd app flutter build apk features authentication using google oauth user profile page uses camera or device media to get an image of the crop preview the image and sends it to api for disease detection result page showing detected disease and remedy generates a pdf report to saveshare predicted disease details option to send the generated result as a notification warning to other users tech stack used python pytorch flask flutter firebase contributors nanda kishor m paiml model api ajay krishna k v flutter dev api hari krishnan uml model data collection antony s johnflutter dev"
example_title: 'Github Cleaned Readme #1'
language:
- en
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-github-repo-tag-generation
Machine Learning model to generate Tags for Github Repositories based on their Documentation [README.md] . This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) fine-tuned on a collection of repositoreis from [Kaggle/vatsalparsaniya/github-repositories-analysis](https://www.kaggle.com/datasets/vatsalparsaniya/github-repositories-analysis). While usually formulated as a multi-label classification problem, this model deals with _tag generation_ as a text2text generation task (inspiration and reference: [fabiochiu/t5-base-tag-generation](https://huggingface.co/fabiochiu/t5-base-tag-generation)).
<br><br>
The Inference API here expects a cleaned readme text, the code for cleaning the readme is also given below.
<br><br>
Finetuning Notebook Reference: [Hugging face summarization notebook](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb).
# How to use the model
Input : Github Repo URL<br>
Output : Tags
Remarks: Ensure the repo has README.<b>md</b>
### Installations
```python
pip install transformers nltk clean-text beautifulsoup4
```
### Code
Imports
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import re
import nltk
nltk.download('punkt')
from cleantext import clean
from bs4 import BeautifulSoup
from markdown import Markdown
import requests
from io import StringIO
import string
```
Preprocessing
```python
# Script to convert Markdown to plain text
# Reference : Stackoverflow == https://stackoverflow.com/questions/761824/python-how-to-convert-markdown-formatted-text-to-text
def unmark_element(element, stream=None):
if stream is None:
stream = StringIO()
if element.text:
stream.write(element.text)
for sub in element:
unmark_element(sub, stream)
if element.tail:
stream.write(element.tail)
return stream.getvalue()
# patching Markdown
Markdown.output_formats["plain"] = unmark_element
__md = Markdown(output_format="plain")
__md.stripTopLevelTags = False
def unmark(text):
return __md.convert(text)
def readme_extractor(github_repo_url):
try:
# Get repo HTML using BeautifulSoup
html_content = requests.get(github['python', 'machine learning', 'ml', 'cnn']_repo_url).text
soup = BeautifulSoup(html_content, "html.parser")
# Get README File URL from Repository
readme_url = "https://github.com/" + soup.find("a",{"title":"README.md"}).get("href")
# Generate raw readme file URL
# https://github.com/rasbt/python-machine-learning-book/blob/master/README.md --> https://raw.githubusercontent.com/rasbt/python-machine-learning-book/master/README.md
readme_raw_url = readme_url.replace("/blob/","/")
readme_raw_url = readme_raw_url.replace("github.com","raw.githubusercontent.com")
https://github.com/Lightning-AI/lightning
readme_html_content = requests.get(readme_raw_url ).text
readme_soup = BeautifulSoup(readme_html_content, "html.parser")
readme_text = readme_soup.get_text()
documentation_text = unmark(readme_text)
return documentation_text
except:
print("FAILED : ",github_repo_url )
return "README_NOT_MARKDOWN"
def clean_readme(readme):
text = clean(readme, no_emoji=True)
lst = re.findall('http://\S+|https://\S+', text)
for i in lst:
text = text.replace(i, '')
text = "".join([i for i in text if i not in string.punctuation])
text = text.lower()
text = text.replace("\n"," ")
return text
```
Postprocess Tags [Removing duplicates]
```python
def post_process_tags(tag_string):
final_tags = []
for tag in tag_string.split(","):
if tag.strip() in final_tags or len(tag.strip()) <=1:
continue
final_tags.append(tag.strip())
return final_tags
```
Main Function
```python
def github_tags_generate(github_repo_url):
readme = readme_extractor(github_repo_url)
readme = clean_readme(readme)
inputs = tokenizer([readme], max_length=1536, truncation=True, return_tensors="pt")
output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10,
max_length=128)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
tags = post_process_tags(decoded_output)
return tags
github_tags_generate("https://github.com/Enter_Repo_URL")
# github_tags_generate("https://github.com/nandakishormpai/Plant_Disease_Detector")
# ['python', 'machine learning', 'ml', 'cnn']
```
## Dataset Preparation
Over the 1000 articles from the dataset, only 870 had tags and the readme was longer than 50 characters. They were filtered out and using BeautifulSoup, README.md was scraped out.
## Intended uses & limitations
The results might contain duplicate tags that must be handled in the postprocessing of results. postprocessing code also given.
## Results
It achieves the following results on the evaluation set:
- Loss: 1.8196
- Rouge1: 25.0142
- Rouge2: 8.1802
- Rougel: 22.77
- Rougelsum: 22.8017
- Gen Len: 19.0
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
peterxyz/dqn-SpaceInvadersNoFrameskip-v4
|
peterxyz
| 2023-02-23T06:20:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-23T02:45:42Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 251.00 +/- 134.31
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga peterxyz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga peterxyz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga peterxyz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 1e-05),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined
|
kevinscaria
| 2023-02-23T06:12:10Z | 80 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"NLP",
"dataset:Yaxin/SemEval2014Task4Raw",
"arxiv:2302.08624",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-23T05:18:24Z |
---
license: mit
tags:
- NLP
datasets:
- Yaxin/SemEval2014Task4Raw
metrics:
- f1
- precision
- recall
pipeline_tag: text2text-generation
---
# ate_tk-instruct-base-def-pos-neg-neut-combined
This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form:
- definition + 2 positive examples + 2 negative examples + 2 neutral examples
The prompt is prepended onto each input review. It is important to note that **this model output was finetuned on samples from both laptops and restaurants domains.**
The code for the official implementation of the paper [**InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis**](https://arxiv.org/abs/2302.08624) can be
found [here](https://github.com/kevinscaria/InstructABSA).
For the ATE subtask, this model is the current SOTA.
## Training data
InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This [dataset](https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools) consists of reviews
from laptops and restaurant domains and their corresponding aspect term and polarity labels.
### BibTeX entry and citation info
If you use this model in your work, please cite the following paper:
```bibtex
@inproceedings{Scaria2023InstructABSAIL,
title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
year={2023}
}
```
|
kevinscaria/ate_tk-instruct-base-def-pos-restaurants
|
kevinscaria
| 2023-02-23T06:09:11Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"NLP",
"dataset:Yaxin/SemEval2014Task4Raw",
"arxiv:2302.08624",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-23T05:51:28Z |
---
license: mit
tags:
- NLP
datasets:
- Yaxin/SemEval2014Task4Raw
metrics:
- f1
- precision
- recall
pipeline_tag: text2text-generation
---
# ate_tk-instruct-base-def-pos-neg-neut-combined
This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form:
- definition + 2 positive examples
The prompt is prepended onto each input review. It is important to note that **this model output was finetuned on samples from the restaurants domains.**
The code for the official implementation of the paper [**InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis**](https://arxiv.org/abs/2302.08624) can be
found [here](https://github.com/kevinscaria/InstructABSA).
For the ATE subtask, this model is the current SOTA.
## Training data
InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This [dataset](https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools) consists of reviews
from laptops and restaurant domains and their corresponding aspect term and polarity labels.
### BibTeX entry and citation info
If you use this model in your work, please cite the following paper:
```bibtex
@inproceedings{Scaria2023InstructABSAIL,
title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
year={2023}
}
```
|
Lenzrix/expmode7
|
Lenzrix
| 2023-02-23T06:08:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-02-23T05:19:22Z |
Hello ndimensional, I just wanted to take a moment to express my gratitude for the incredible 3D model you have created. Your work has inspired me, and I recently had the pleasure of using your model in one of my own projects. I was amazed by the level of detail and realism in your model, and it truly brought my work to life. Thank you for sharing your talents with the world and for providing such amazing resources for fellow 3D designers. I would like to give credit where credit is due, so I have included a link to your original model in my project. Once again, thank you so much for your amazing work! Best regards, Lenzrix.
Link to original author: https://civitai.com/user/ndimensional
This is just optional download for huggingface.
|
ongknsro/neko-classifier
|
ongknsro
| 2023-02-23T06:06:12Z | 0 | 0 | null |
[
"image-classification",
"en",
"license:gpl-3.0",
"region:us"
] |
image-classification
| 2023-01-24T18:17:16Z |
---
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
license: gpl-3.0
---
### This repo will host all iterations of models from our neko-classifier project.
|
Airic/Kenshi
|
Airic
| 2023-02-23T06:03:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-23T05:55:04Z |
---
license: creativeml-openrail-m
---
|
evincent18/distilbert-base-uncased-finetuned-imdb
|
evincent18
| 2023-02-23T06:00:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-23T05:52:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Dabid/abusive-tagalog-profanity-detection
|
Dabid
| 2023-02-23T05:58:13Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-23T05:20:04Z |
---
license: gpl-3.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test2
results: []
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test2
This model is a fine-tuned version of [jcblaise/bert-tagalog-base-cased](https://huggingface.co/jcblaise/bert-tagalog-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4185
- Accuracy: 0.8669
- Precision: 0.8249
- Recall: 0.8612
- F1: 0.8426
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 172 | 0.3674 | 0.8444 | 0.8014 | 0.8295 | 0.8152 |
| No log | 2.0 | 344 | 0.3508 | 0.8542 | 0.8235 | 0.8243 | 0.8239 |
| 0.2992 | 3.0 | 516 | 0.3643 | 0.8564 | 0.8596 | 0.7803 | 0.8181 |
| 0.2992 | 4.0 | 688 | 0.3639 | 0.8622 | 0.8155 | 0.8620 | 0.8381 |
| 0.2992 | 5.0 | 860 | 0.3803 | 0.864 | 0.8316 | 0.8418 | 0.8367 |
| 0.1733 | 6.0 | 1032 | 0.3969 | 0.8702 | 0.8352 | 0.8550 | 0.8450 |
| 0.1733 | 7.0 | 1204 | 0.4185 | 0.8669 | 0.8249 | 0.8612 | 0.8426 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
jngan/distilbert-base-uncased-finetuned-emotion
|
jngan
| 2023-02-23T05:41:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-21T04:20:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249466339767543
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2161
- Accuracy: 0.925
- F1: 0.9249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8157 | 1.0 | 250 | 0.3191 | 0.9015 | 0.8973 |
| 0.247 | 2.0 | 500 | 0.2161 | 0.925 | 0.9249 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
kevinscaria/ate_tk-instruct-base-def-pos-combined
|
kevinscaria
| 2023-02-23T05:22:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"NLP",
"dataset:Yaxin/SemEval2014Task4Raw",
"arxiv:2302.08624",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-23T04:59:01Z |
---
license: mit
tags:
- NLP
datasets:
- Yaxin/SemEval2014Task4Raw
metrics:
- f1
- precision
- recall
pipeline_tag: text2text-generation
---
# ate_tk-instruct-base-def-pos-combined
This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form:
- definition + 2 positive examples
The prompt is prepended onto each input review. It is important to note that **this model output was finetuned on samples from both laptops and restaurants domains.**
The code for the official implementation of the paper [**InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis**](https://arxiv.org/abs/2302.08624) can be
found [here](https://github.com/kevinscaria/InstructABSA).
For the ATE subtask, this model is the current SOTA.
## Training data
InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This [dataset](https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools) consists of reviews
from laptops and restaurant domains and their corresponding aspect term and polarity labels.
### BibTeX entry and citation info
If you use this model in your work, please cite the following paper:
```bibtex
@inproceedings{Scaria2023InstructABSAIL,
title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
year={2023}
}
```
|
LowRAs/mfbaseLoRA
|
LowRAs
| 2023-02-23T05:19:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-23T04:34:14Z |
---
license: creativeml-openrail-m
---
|
Arka1794/SD-sentiment-model-BERT-v1
|
Arka1794
| 2023-02-23T05:18:24Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-23T04:45:54Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: SD-sentiment-model-BERT-v1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# SD-sentiment-model-BERT-v1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0686
- Train Accuracy: 0.9786
- Validation Loss: 0.4576
- Validation Accuracy: 0.8860
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3289 | 0.8543 | 0.3989 | 0.8560 | 0 |
| 0.0686 | 0.9786 | 0.4576 | 0.8860 | 1 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
yasakoko/NoAI-Diffusion
|
yasakoko
| 2023-02-23T05:02:46Z | 20 | 7 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"NoAI",
"AntiAI",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-17T11:28:03Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- NoAI
- AntiAI
---
## NoAI-Diffusion v1.2
Updates: prompt now is **antiai logo**
# .CKPT at /models
# Stable Diffusion 1.4 finetuned with a lot of NoAI/AntiAI images. Have fun 🤗
## Sample image
v1.0 → normal
v1.1 → *creative*
v1.2 → __clear letters__
NoAI-Diffusion v1.0
||||
|-|-|-|
||||
||||
## Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "Kokohachi/NoAI-Diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to("cuda")
prompt = "a photo of antiai logo"
image = pipe(prompt).images[0]
image.save("noai.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
|
MarcusLee/bert-finetuned-ner
|
MarcusLee
| 2023-02-23T04:33:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-23T04:11:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9387958202023553
- name: Recall
type: recall
value: 0.9525412319084483
- name: F1
type: f1
value: 0.9456185782307241
- name: Accuracy
type: accuracy
value: 0.9870783540354389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Precision: 0.9388
- Recall: 0.9525
- F1: 0.9456
- Accuracy: 0.9871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0836 | 1.0 | 1756 | 0.0650 | 0.9214 | 0.9355 | 0.9284 | 0.9822 |
| 0.0347 | 2.0 | 3512 | 0.0619 | 0.9238 | 0.9465 | 0.9350 | 0.9856 |
| 0.017 | 3.0 | 5268 | 0.0592 | 0.9388 | 0.9525 | 0.9456 | 0.9871 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
pszemraj/opt-peter-2.7B-sharded
|
pszemraj
| 2023-02-23T04:28:46Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"non-commercial",
"dialogue",
"chatbot",
"ai-msgbot",
"license:apache-2.0",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-12-20T03:34:57Z |
---
license:
- apache-2.0
- other
tags:
- generated_from_trainer
- text-generation
- opt
- non-commercial
- dialogue
- chatbot
- ai-msgbot
inference: false
---
# pszemraj/opt-peter-2.7B-sharded
The same thing as [pszemraj/opt-peter-2.7B](https://huggingface.co/pszemraj/opt-peter-2.7B) except I sharded it. Please refer to the model card previously linked for all details.
---
|
dp66/ppo-LunarLander-v2
|
dp66
| 2023-02-23T03:56:44Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-23T03:56:19Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.84 +/- 20.69
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Dainong2/bert-finetuned-ner
|
Dainong2
| 2023-02-23T02:44:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-16T04:23:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.93658940397351
- name: Recall
type: recall
value: 0.9520363513968361
- name: F1
type: f1
value: 0.944249707895176
- name: Accuracy
type: accuracy
value: 0.987622888090893
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0571
- Precision: 0.9366
- Recall: 0.9520
- F1: 0.9442
- Accuracy: 0.9876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0852 | 1.0 | 1756 | 0.0642 | 0.9139 | 0.9323 | 0.9230 | 0.9821 |
| 0.034 | 2.0 | 3512 | 0.0607 | 0.9275 | 0.9498 | 0.9386 | 0.9858 |
| 0.0168 | 3.0 | 5268 | 0.0571 | 0.9366 | 0.9520 | 0.9442 | 0.9876 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
NielsPeng/sd-class-butterflies-32
|
NielsPeng
| 2023-02-23T02:04:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-02-23T02:03:48Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('NielsPeng/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
pittawat/rl_course_vizdoom_health_gathering_supreme
|
pittawat
| 2023-02-23T01:23:05Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:53:18Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 14.47 +/- 4.97
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r pittawat/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
ziadA123/trainModel_p1
|
ziadA123
| 2023-02-22T23:52:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:ziadA123/autotrain-data-test_prepreocessing2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-22T23:51:08Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ziadA123/autotrain-data-test_prepreocessing2
co2_eq_emissions:
emissions: 0.009254993806045749
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3672198102
- CO2 Emissions (in grams): 0.0093
## Validation Metrics
- Loss: 0.112
- Accuracy: 0.972
- Precision: 0.964
- Recall: 0.980
- AUC: 0.990
- F1: 0.972
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ziadA123/autotrain-test_prepreocessing2-3672198102
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ziadA123/autotrain-test_prepreocessing2-3672198102", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ziadA123/autotrain-test_prepreocessing2-3672198102", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
danielv835/santacoder-finetuned-the-stack-rust-test1
|
danielv835
| 2023-02-22T23:42:54Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-21T19:26:40Z |
---
license: openrail
tags:
- generated_from_trainer
model-index:
- name: santacoder-finetuned-the-stack-rust-test1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# santacoder-finetuned-the-stack-rust-test1
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6679 | 0.3 | 3 | 1.2840 |
| 1.5399 | 0.6 | 6 | 1.3103 |
| 0.3854 | 0.9 | 9 | 1.2870 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
cleanrl/Atlantis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:38:43Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Atlantis-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:38:42Z |
---
tags:
- Atlantis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Atlantis-v5
type: Atlantis-v5
metrics:
- type: mean_reward
value: 936050.00 +/- 36868.18
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Atlantis-v5**
This is a trained model of a PPO agent playing Atlantis-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Atlantis-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Atlantis-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Atlantis-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Berzerk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:33:34Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Berzerk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:33:33Z |
---
tags:
- Berzerk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Berzerk-v5
type: Berzerk-v5
metrics:
- type: mean_reward
value: 1389.00 +/- 331.50
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Berzerk-v5**
This is a trained model of a PPO agent playing Berzerk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Berzerk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Berzerk-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Berzerk-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:30:51Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BattleZone-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:30:50Z |
---
tags:
- BattleZone-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BattleZone-v5
type: BattleZone-v5
metrics:
- type: mean_reward
value: 32600.00 +/- 4200.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BattleZone-v5**
This is a trained model of a PPO agent playing BattleZone-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id BattleZone-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BattleZone-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BattleZone-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:29:36Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BattleZone-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:29:34Z |
---
tags:
- BattleZone-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BattleZone-v5
type: BattleZone-v5
metrics:
- type: mean_reward
value: 29600.00 +/- 7269.11
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BattleZone-v5**
This is a trained model of a PPO agent playing BattleZone-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id BattleZone-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BattleZone-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BattleZone-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BattleZone-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/BankHeist-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:25:14Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"BankHeist-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:25:13Z |
---
tags:
- BankHeist-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BankHeist-v5
type: BankHeist-v5
metrics:
- type: mean_reward
value: 1188.00 +/- 66.30
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BankHeist-v5**
This is a trained model of a PPO agent playing BankHeist-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id BankHeist-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BankHeist-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BankHeist-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Alien-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:24:12Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Alien-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:24:11Z |
---
tags:
- Alien-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Alien-v5
type: Alien-v5
metrics:
- type: mean_reward
value: 1555.00 +/- 1111.53
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Alien-v5**
This is a trained model of a PPO agent playing Alien-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Alien-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Alien-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Alien-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Alien-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Alien-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Alien-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:21:49Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Asteroids-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:21:48Z |
---
tags:
- Asteroids-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asteroids-v5
type: Asteroids-v5
metrics:
- type: mean_reward
value: 2855.00 +/- 1100.61
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asteroids-v5**
This is a trained model of a PPO agent playing Asteroids-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Asteroids-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asteroids-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asteroids-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:20:43Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Asteroids-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:20:41Z |
---
tags:
- Asteroids-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asteroids-v5
type: Asteroids-v5
metrics:
- type: mean_reward
value: 3370.00 +/- 645.55
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asteroids-v5**
This is a trained model of a PPO agent playing Asteroids-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Asteroids-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asteroids-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asteroids-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Amidar-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:20:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Amidar-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:20:33Z |
---
tags:
- Amidar-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Amidar-v5
type: Amidar-v5
metrics:
- type: mean_reward
value: 942.20 +/- 189.60
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Amidar-v5**
This is a trained model of a PPO agent playing Amidar-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Amidar-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Amidar-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Amidar-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:20:16Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Assault-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:20:15Z |
---
tags:
- Assault-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Assault-v5
type: Assault-v5
metrics:
- type: mean_reward
value: 13987.40 +/- 6217.99
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Assault-v5**
This is a trained model of a PPO agent playing Assault-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Assault-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Assault-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Assault-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:19:52Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Asterix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:19:51Z |
---
tags:
- Asterix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asterix-v5
type: Asterix-v5
metrics:
- type: mean_reward
value: 8260.00 +/- 4624.54
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asterix-v5**
This is a trained model of a PPO agent playing Asterix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Asterix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asterix-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:19:34Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Asterix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:19:32Z |
---
tags:
- Asterix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asterix-v5
type: Asterix-v5
metrics:
- type: mean_reward
value: 13420.00 +/- 6376.49
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asterix-v5**
This is a trained model of a PPO agent playing Asterix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Asterix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asterix-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:19:16Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Asterix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:19:15Z |
---
tags:
- Asterix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asterix-v5
type: Asterix-v5
metrics:
- type: mean_reward
value: 13460.00 +/- 8569.39
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asterix-v5**
This is a trained model of a PPO agent playing Asterix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Asterix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asterix-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Tennis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:14:53Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Tennis-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:14:51Z |
---
tags:
- Tennis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tennis-v5
type: Tennis-v5
metrics:
- type: mean_reward
value: -1.80 +/- 2.44
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tennis-v5**
This is a trained model of a PPO agent playing Tennis-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Tennis-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Tennis-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Tennis-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Zaxxon-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:11:47Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Zaxxon-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:11:46Z |
---
tags:
- Zaxxon-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Zaxxon-v5
type: Zaxxon-v5
metrics:
- type: mean_reward
value: 19920.00 +/- 3871.90
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Zaxxon-v5**
This is a trained model of a PPO agent playing Zaxxon-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Zaxxon-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
alvin0220/bert-finetuned-ner
|
alvin0220
| 2023-02-22T23:11:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-22T22:48:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9480193882667558
- name: Recall
type: recall
value: 0.9545607539548974
- name: F1
type: f1
value: 0.9512788259958073
- name: Accuracy
type: accuracy
value: 0.9917448697480628
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0400
- Precision: 0.9480
- Recall: 0.9546
- F1: 0.9513
- Accuracy: 0.9917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0529 | 1.0 | 1756 | 0.0418 | 0.9390 | 0.9423 | 0.9406 | 0.9901 |
| 0.0197 | 2.0 | 3512 | 0.0436 | 0.9338 | 0.9493 | 0.9415 | 0.9904 |
| 0.0109 | 3.0 | 5268 | 0.0400 | 0.9480 | 0.9546 | 0.9513 | 0.9917 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
cleanrl/VideoPinball-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:09:48Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"VideoPinball-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:09:47Z |
---
tags:
- VideoPinball-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: VideoPinball-v5
type: VideoPinball-v5
metrics:
- type: mean_reward
value: 59784.80 +/- 56992.54
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **VideoPinball-v5**
This is a trained model of a PPO agent playing VideoPinball-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id VideoPinball-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/VideoPinball-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id VideoPinball-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'VideoPinball-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
Andrei95/autotrain-jobberta-23-3671398065
|
Andrei95
| 2023-02-22T23:09:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:Andrei95/autotrain-data-jobberta-23",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-22T23:06:49Z |
---
tags:
- autotrain
- token-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Andrei95/autotrain-data-jobberta-23
co2_eq_emissions:
emissions: 4.051202274340627
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 3671398065
- CO2 Emissions (in grams): 4.0512
## Validation Metrics
- Loss: 0.248
- Accuracy: 0.915
- Precision: 0.570
- Recall: 0.639
- F1: 0.603
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Andrei95/autotrain-jobberta-23-3671398065
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("Andrei95/autotrain-jobberta-23-3671398065", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Andrei95/autotrain-jobberta-23-3671398065", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:07:20Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"YarsRevenge-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:07:19Z |
---
tags:
- YarsRevenge-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: YarsRevenge-v5
type: YarsRevenge-v5
metrics:
- type: mean_reward
value: 91629.60 +/- 9983.31
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **YarsRevenge-v5**
This is a trained model of a PPO agent playing YarsRevenge-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id YarsRevenge-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id YarsRevenge-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'YarsRevenge-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:07:08Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"YarsRevenge-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:07:07Z |
---
tags:
- YarsRevenge-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: YarsRevenge-v5
type: YarsRevenge-v5
metrics:
- type: mean_reward
value: 83041.20 +/- 8517.12
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **YarsRevenge-v5**
This is a trained model of a PPO agent playing YarsRevenge-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id YarsRevenge-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/YarsRevenge-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id YarsRevenge-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'YarsRevenge-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/WizardOfWor-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T23:04:26Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"WizardOfWor-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:04:25Z |
---
tags:
- WizardOfWor-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: WizardOfWor-v5
type: WizardOfWor-v5
metrics:
- type: mean_reward
value: 10170.00 +/- 7145.08
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **WizardOfWor-v5**
This is a trained model of a PPO agent playing WizardOfWor-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id WizardOfWor-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id WizardOfWor-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'WizardOfWor-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T23:02:00Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Venture-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:01:59Z |
---
tags:
- Venture-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Venture-v5
type: Venture-v5
metrics:
- type: mean_reward
value: 1030.00 +/- 449.56
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Venture-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T23:01:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Venture-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T23:01:56Z |
---
tags:
- Venture-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Venture-v5
type: Venture-v5
metrics:
- type: mean_reward
value: 1190.00 +/- 301.50
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Venture-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/TimePilot-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:59:34Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"TimePilot-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:59:33Z |
---
tags:
- TimePilot-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: TimePilot-v5
type: TimePilot-v5
metrics:
- type: mean_reward
value: 10530.00 +/- 2893.80
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **TimePilot-v5**
This is a trained model of a PPO agent playing TimePilot-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id TimePilot-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/TimePilot-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/TimePilot-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/TimePilot-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id TimePilot-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'TimePilot-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:57:38Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Surround-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:57:36Z |
---
tags:
- Surround-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Surround-v5
type: Surround-v5
metrics:
- type: mean_reward
value: -4.20 +/- 2.96
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Surround-v5**
This is a trained model of a PPO agent playing Surround-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Surround-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Surround-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Surround-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:57:21Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Surround-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:57:20Z |
---
tags:
- Surround-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Surround-v5
type: Surround-v5
metrics:
- type: mean_reward
value: -1.00 +/- 4.73
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Surround-v5**
This is a trained model of a PPO agent playing Surround-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Surround-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Surround-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Surround-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:55:32Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"StarGunner-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:55:31Z |
---
tags:
- StarGunner-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: StarGunner-v5
type: StarGunner-v5
metrics:
- type: mean_reward
value: 72230.00 +/- 8061.02
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **StarGunner-v5**
This is a trained model of a PPO agent playing StarGunner-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id StarGunner-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id StarGunner-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'StarGunner-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/SpaceInvaders-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:55:32Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"SpaceInvaders-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:55:31Z |
---
tags:
- SpaceInvaders-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvaders-v5
type: SpaceInvaders-v5
metrics:
- type: mean_reward
value: 3357.00 +/- 3813.24
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **SpaceInvaders-v5**
This is a trained model of a PPO agent playing SpaceInvaders-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id SpaceInvaders-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id SpaceInvaders-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'SpaceInvaders-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:55:24Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"StarGunner-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:55:22Z |
---
tags:
- StarGunner-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: StarGunner-v5
type: StarGunner-v5
metrics:
- type: mean_reward
value: 65590.00 +/- 3888.56
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **StarGunner-v5**
This is a trained model of a PPO agent playing StarGunner-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id StarGunner-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id StarGunner-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'StarGunner-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
UMUTeam/galician_capitalization_punctuation_restoration
|
UMUTeam
| 2023-02-22T22:49:43Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"gl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-20T17:21:09Z |
---
widget:
- text: chámome javier e como te chamas
example_title: Example1
- text: o meu equipo favorito é a royal society
example_title: Example2
- text: bo nadal
example_title: Example3
- text: benvido a murcia
example_title: Example4
language:
- gl
---
|
cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:45:42Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:45:40Z |
---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 1376.00 +/- 920.82
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Solaris-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:45:05Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:45:03Z |
---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 2300.00 +/- 721.17
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Solaris-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
huggingtweets/welcomeunknown
|
huggingtweets
| 2023-02-22T22:44:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-20T10:32:13Z |
---
language: en
thumbnail: http://www.huggingtweets.com/welcomeunknown/1677105894820/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1568130101832994823/wg1Q3gV3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">b e a r 🤍⃤</div>
<div style="text-align: center; font-size: 14px;">@welcomeunknown</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from b e a r 🤍⃤.
| Data | b e a r 🤍⃤ |
| --- | --- |
| Tweets downloaded | 3044 |
| Retweets | 1141 |
| Short tweets | 217 |
| Tweets kept | 1686 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9ep0mx0c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @welcomeunknown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/na2bgp7d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/na2bgp7d/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/welcomeunknown')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:44:24Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:44:22Z |
---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 1604.00 +/- 1006.97
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Solaris-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:44:21Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Robotank-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:44:20Z |
---
tags:
- Robotank-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Robotank-v5
type: Robotank-v5
metrics:
- type: mean_reward
value: 35.70 +/- 7.56
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Robotank-v5**
This is a trained model of a PPO agent playing Robotank-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Robotank-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Robotank-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Robotank-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:44:13Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Robotank-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:44:12Z |
---
tags:
- Robotank-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Robotank-v5
type: Robotank-v5
metrics:
- type: mean_reward
value: 32.30 +/- 4.78
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Robotank-v5**
This is a trained model of a PPO agent playing Robotank-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Robotank-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Robotank-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Robotank-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:43:56Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Robotank-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:43:55Z |
---
tags:
- Robotank-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Robotank-v5
type: Robotank-v5
metrics:
- type: mean_reward
value: 30.90 +/- 7.52
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Robotank-v5**
This is a trained model of a PPO agent playing Robotank-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Robotank-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Robotank-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Robotank-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Robotank-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:39:23Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Riverraid-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:39:21Z |
---
tags:
- Riverraid-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Riverraid-v5
type: Riverraid-v5
metrics:
- type: mean_reward
value: 9542.00 +/- 323.63
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Riverraid-v5**
This is a trained model of a PPO agent playing Riverraid-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Riverraid-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Riverraid-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Riverraid-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
opiljain/autotrain-flags-3670798043
|
opiljain
| 2023-02-22T22:38:52Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:opiljain/autotrain-data-flags",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-22T22:35:42Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- opiljain/autotrain-data-flags
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 4.145044266449912
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3670798043
- CO2 Emissions (in grams): 4.1450
## Validation Metrics
- Loss: 0.203
- Accuracy: 0.949
- Macro F1: 0.954
- Micro F1: 0.949
- Weighted F1: 0.950
- Macro Precision: 0.956
- Micro Precision: 0.949
- Weighted Precision: 0.955
- Macro Recall: 0.956
- Micro Recall: 0.949
- Weighted Recall: 0.949
|
cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:38:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Riverraid-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:38:34Z |
---
tags:
- Riverraid-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Riverraid-v5
type: Riverraid-v5
metrics:
- type: mean_reward
value: 15081.00 +/- 1308.48
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Riverraid-v5**
This is a trained model of a PPO agent playing Riverraid-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Riverraid-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Riverraid-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Riverraid-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:38:15Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Riverraid-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:38:14Z |
---
tags:
- Riverraid-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Riverraid-v5
type: Riverraid-v5
metrics:
- type: mean_reward
value: 10674.00 +/- 998.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Riverraid-v5**
This is a trained model of a PPO agent playing Riverraid-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Riverraid-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Riverraid-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Riverraid-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/PrivateEye-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:38:11Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"PrivateEye-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:38:10Z |
---
tags:
- PrivateEye-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PrivateEye-v5
type: PrivateEye-v5
metrics:
- type: mean_reward
value: 60.00 +/- 48.99
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **PrivateEye-v5**
This is a trained model of a PPO agent playing PrivateEye-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id PrivateEye-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/PrivateEye-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/PrivateEye-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/PrivateEye-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id PrivateEye-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'PrivateEye-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:37:54Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Skiing-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:37:52Z |
---
tags:
- Skiing-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Skiing-v5
type: Skiing-v5
metrics:
- type: mean_reward
value: -8987.20 +/- 22.82
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Skiing-v5**
This is a trained model of a PPO agent playing Skiing-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Skiing-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Skiing-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Skiing-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:37:23Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Skiing-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:37:22Z |
---
tags:
- Skiing-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Skiing-v5
type: Skiing-v5
metrics:
- type: mean_reward
value: -8987.20 +/- 22.82
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Skiing-v5**
This is a trained model of a PPO agent playing Skiing-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Skiing-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Skiing-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Skiing-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Skiing-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Seaquest-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:36:35Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Seaquest-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:36:34Z |
---
tags:
- Seaquest-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Seaquest-v5
type: Seaquest-v5
metrics:
- type: mean_reward
value: 960.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Seaquest-v5**
This is a trained model of a PPO agent playing Seaquest-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Seaquest-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Seaquest-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Seaquest-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Pitfall-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:35:12Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pitfall-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:35:11Z |
---
tags:
- Pitfall-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pitfall-v5
type: Pitfall-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Pitfall-v5**
This is a trained model of a PPO agent playing Pitfall-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Pitfall-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Pitfall-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Pitfall-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Pitfall-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Pitfall-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Pitfall-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
RamonAnkersmit/rl_course_doom_deadly_corridor
|
RamonAnkersmit
| 2023-02-22T22:35:02Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:34:51Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_deadly_corridor
type: doom_deadly_corridor
metrics:
- type: mean_reward
value: 10.42 +/- 8.36
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_deadly_corridor** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r RamonAnkersmit/rl_course_doom_deadly_corridor
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_deadly_corridor --train_dir=./train_dir --experiment=rl_course_doom_deadly_corridor
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_deadly_corridor --train_dir=./train_dir --experiment=rl_course_doom_deadly_corridor --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
cleanrl/Qbert-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:34:59Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Qbert-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:34:58Z |
---
tags:
- Qbert-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Qbert-v5
type: Qbert-v5
metrics:
- type: mean_reward
value: 15787.50 +/- 600.86
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Qbert-v5**
This is a trained model of a PPO agent playing Qbert-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Qbert-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Qbert-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Qbert-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Qbert-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Qbert-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Qbert-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/NameThisGame-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:34:00Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"NameThisGame-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:33:59Z |
---
tags:
- NameThisGame-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: NameThisGame-v5
type: NameThisGame-v5
metrics:
- type: mean_reward
value: 11001.00 +/- 2712.99
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **NameThisGame-v5**
This is a trained model of a PPO agent playing NameThisGame-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id NameThisGame-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id NameThisGame-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'NameThisGame-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Pong-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:32:18Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Pong-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:32:17Z |
---
tags:
- Pong-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v5
type: Pong-v5
metrics:
- type: mean_reward
value: 20.80 +/- 0.40
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Pong-v5**
This is a trained model of a PPO agent playing Pong-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Pong-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Pong-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Pong-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Pong-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Pong-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Pong-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Phoenix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:32:05Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Phoenix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:32:04Z |
---
tags:
- Phoenix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Phoenix-v5
type: Phoenix-v5
metrics:
- type: mean_reward
value: 51456.00 +/- 24649.75
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Phoenix-v5**
This is a trained model of a PPO agent playing Phoenix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Phoenix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Phoenix-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Phoenix-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
Andrei95/autotrain-jobberta-20-3670698025
|
Andrei95
| 2023-02-22T22:23:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain",
"unk",
"dataset:Andrei95/autotrain-data-jobberta-20",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-02-22T22:19:52Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Andrei95/autotrain-data-jobberta-20
co2_eq_emissions:
emissions: 0.03057606391853882
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 3670698025
- CO2 Emissions (in grams): 0.0306
## Validation Metrics
- Loss: 0.235
- Accuracy: 0.917
- Precision: 0.602
- Recall: 0.703
- F1: 0.649
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Andrei95/autotrain-jobberta-20-3670698025
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("Andrei95/autotrain-jobberta-20-3670698025", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Andrei95/autotrain-jobberta-20-3670698025", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
cleanrl/DoubleDunk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:13:46Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"DoubleDunk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:13:45Z |
---
tags:
- DoubleDunk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DoubleDunk-v5
type: DoubleDunk-v5
metrics:
- type: mean_reward
value: 0.00 +/- 1.55
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DoubleDunk-v5**
This is a trained model of a PPO agent playing DoubleDunk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id DoubleDunk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DoubleDunk-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id DoubleDunk-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'DoubleDunk-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:08:48Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:08:46Z |
---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 33.00 +/- 4.20
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id FishingDerby-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:08:43Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Freeway-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:08:41Z |
---
tags:
- Freeway-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Freeway-v5
type: Freeway-v5
metrics:
- type: mean_reward
value: 22.20 +/- 0.98
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Freeway-v5**
This is a trained model of a PPO agent playing Freeway-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Freeway-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Freeway-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Freeway-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:08:37Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Freeway-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:08:35Z |
---
tags:
- Freeway-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Freeway-v5
type: Freeway-v5
metrics:
- type: mean_reward
value: 22.20 +/- 1.08
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Freeway-v5**
This is a trained model of a PPO agent playing Freeway-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Freeway-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Freeway-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Freeway-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Freeway-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:08:33Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:08:31Z |
---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 33.20 +/- 5.40
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id FishingDerby-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:08:22Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:08:20Z |
---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 29.30 +/- 7.67
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id FishingDerby-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
dn-gh/CleanRLPPO-LunarLander-v2
|
dn-gh
| 2023-02-22T22:08:01Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:07:30Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 96.94 +/- 75.83
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.00025
'num_envs': 16
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 64
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': 0.015
'repo_id': 'dn-gh/CleanRLPPO-LunarLander-v2'
'batch_size': 16384
'minibatch_size': 256}
```
|
cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:04:08Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Breakout-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:04:06Z |
---
tags:
- Breakout-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 704.30 +/- 190.04
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Breakout-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Breakout-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T22:04:05Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Breakout-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:04:03Z |
---
tags:
- Breakout-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 568.20 +/- 203.95
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Breakout-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Breakout-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Frostbite-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:02:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Frostbite-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:02:17Z |
---
tags:
- Frostbite-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Frostbite-v5
type: Frostbite-v5
metrics:
- type: mean_reward
value: 4538.00 +/- 1541.88
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Frostbite-v5**
This is a trained model of a PPO agent playing Frostbite-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Frostbite-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Frostbite-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Frostbite-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T22:01:00Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ChopperCommand-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:00:59Z |
---
tags:
- ChopperCommand-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ChopperCommand-v5
type: ChopperCommand-v5
metrics:
- type: mean_reward
value: 5990.00 +/- 3176.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **ChopperCommand-v5**
This is a trained model of a PPO agent playing ChopperCommand-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id ChopperCommand-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id ChopperCommand-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'ChopperCommand-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T22:00:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ChopperCommand-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T22:00:55Z |
---
tags:
- ChopperCommand-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ChopperCommand-v5
type: ChopperCommand-v5
metrics:
- type: mean_reward
value: 6170.00 +/- 1495.36
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **ChopperCommand-v5**
This is a trained model of a PPO agent playing ChopperCommand-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id ChopperCommand-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/ChopperCommand-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id ChopperCommand-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'ChopperCommand-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T21:58:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Assault-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T21:58:56Z |
---
tags:
- Assault-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Assault-v5
type: Assault-v5
metrics:
- type: mean_reward
value: 15510.50 +/- 6527.50
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Assault-v5**
This is a trained model of a PPO agent playing Assault-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Assault-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Assault-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Assault-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Defender-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
cleanrl
| 2023-02-22T21:58:19Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Defender-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T21:58:18Z |
---
tags:
- Defender-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Defender-v5
type: Defender-v5
metrics:
- type: mean_reward
value: 56580.00 +/- 9260.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Defender-v5**
This is a trained model of a PPO agent playing Defender-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Defender-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Defender-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Defender-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/CrazyClimber-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3
|
cleanrl
| 2023-02-22T21:58:07Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"CrazyClimber-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T21:58:05Z |
---
tags:
- CrazyClimber-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CrazyClimber-v5
type: CrazyClimber-v5
metrics:
- type: mean_reward
value: 117960.00 +/- 7942.32
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **CrazyClimber-v5**
This is a trained model of a PPO agent playing CrazyClimber-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id CrazyClimber-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id CrazyClimber-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'CrazyClimber-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2
|
cleanrl
| 2023-02-22T21:56:56Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Assault-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T21:56:55Z |
---
tags:
- Assault-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Assault-v5
type: Assault-v5
metrics:
- type: mean_reward
value: 10734.90 +/- 5524.01
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Assault-v5**
This is a trained model of a PPO agent playing Assault-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Assault-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Assault-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Assault-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Assault-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
Mobius-labs/JenL_demo1
|
Mobius-labs
| 2023-02-22T21:55:46Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"arxiv:1910.09700",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-22T20:26:15Z |
---
library_name: diffusers
base_model: runwayml/stable-diffusion-v1-5
pipeline_tag: text-to-image
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing [optional]
[More Information Needed]
### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
pjheslin/shak-gpt-neo-125M
|
pjheslin
| 2023-02-22T21:48:30Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-22T21:40:14Z |
---
license: gpl-3.0
---
ElutherAI's gpt-neo-125M model fine-tuned on a text file containing the works of Shakespeare from Project Gutenberg:
`https://www.gutenberg.org/cache/epub/100/pg100.txt`
It was trained for 50 epochs, using the happytransformer library.
|
Schwarzschild009/polgrad-Pixelcopter-PLE-v0
|
Schwarzschild009
| 2023-02-22T21:43:36Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T14:47:15Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: polgrad-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 47.50 +/- 36.82
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1
|
vwxyzjn
| 2023-02-22T21:30:59Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"Breakout-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T20:43:31Z |
---
tags:
- Breakout-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 679.80 +/- 208.17
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id Breakout-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --save-model --upload-model --env-id Breakout-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': '',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL',
'world_size': 2}
```
|
RamonAnkersmit/rl_course_vizdoom_health_gathering_supreme_v2
|
RamonAnkersmit
| 2023-02-22T21:26:20Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T21:26:05Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.88 +/- 4.25
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r RamonAnkersmit/rl_course_vizdoom_health_gathering_supreme_v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
sam2ai/flan-t5-base-samsum
|
sam2ai
| 2023-02-22T21:12:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-22T20:56:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: flan-t5-base-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3910
- eval_rouge1: 47.2277
- eval_rouge2: 23.3544
- eval_rougeL: 39.3705
- eval_rougeLsum: 43.3621
- eval_gen_len: 17.4188
- eval_runtime: 143.4548
- eval_samples_per_second: 5.709
- eval_steps_per_second: 0.718
- epoch: 1.11
- step: 2038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
babysquid/LoRA
|
babysquid
| 2023-02-22T21:12:03Z | 0 | 0 | null |
[
"ja",
"region:us"
] | null | 2023-02-22T20:44:38Z |
---
language:
- ja
---
# LamiaLoveless.safetensors
キーワードは lamial です。
# IbisDouglas.safetensors
キーワードは ibs です。
# ExcellenBrowning.safetensors
キーワードは excln です。
どれもゲーム内の立ち絵6枚ぐらいで学習しているので非常にクオリティは低いです。
LoRAは0.4～0.6ぐらいにするとあまりクオリティを落とさず出力できると思います。
|
GrimReaperSam/rl_course_vizdoom_health_gathering_supreme
|
GrimReaperSam
| 2023-02-22T21:05:10Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-22T20:32:57Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.55 +/- 5.33
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r GrimReaperSam/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
cl-wood/distilbert-base-uncased-finetuned-imdb
|
cl-wood
| 2023-02-22T21:02:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-21T14:49:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6963 | 1.0 | 157 | 2.5091 |
| 2.5737 | 2.0 | 314 | 2.4515 |
| 2.5496 | 3.0 | 471 | 2.3946 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0.dev20230220
- Tokenizers 0.13.2
|
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