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"__module__": "stable_baselines3.her.her_replay_buffer",
"__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}",
"__doc__": "\n Hindsight Experience Replay (HER) buffer.\n Paper: https://arxiv.org/abs/1707.01495\n\n Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n\n .. note::\n\n Compared to other implementations, the ``future`` goal sampling strategy is inclusive:\n the current transition can be used when re-sampling.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param env: The training environment\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n :param n_sampled_goal: Number of virtual transitions to create per real transition,\n by sampling new goals.\n :param goal_selection_strategy: Strategy for sampling goals for replay.\n One of ['episode', 'final', 'future']\n :param copy_info_dict: Whether to copy the info dictionary and pass it to\n ``compute_reward()`` method.\n Please note that the copy may cause a slowdown.\n False by default.\n ",
"__init__": "<function HerReplayBuffer.__init__ at 0x000001BDEC6FC720>",
"__getstate__": "<function HerReplayBuffer.__getstate__ at 0x000001BDEC6FC7C0>",
"__setstate__": "<function HerReplayBuffer.__setstate__ at 0x000001BDEC6FC860>",
"set_env": "<function HerReplayBuffer.set_env at 0x000001BDEC6FC900>",
"add": "<function HerReplayBuffer.add at 0x000001BDEC6FCA40>",
"_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x000001BDEC6FCAE0>",
"sample": "<function HerReplayBuffer.sample at 0x000001BDEC6FCB80>",
"_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x000001BDEC6FCC20>",
"_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x000001BDEC6FCCC0>",
"_sample_goals": "<function HerReplayBuffer._sample_goals at 0x000001BDEC6FCD60>",
"truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x000001BDEC6FCE00>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x000001BDEC7085C0>"
},
"replay_buffer_kwargs": {
"n_sampled_goal": 4,
"goal_selection_strategy": "future"
},
"n_steps": 1,
"train_freq": {
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
},
"use_sde_at_warmup": false,
"target_entropy": -4.0,
"ent_coef": "auto",
"target_update_interval": 1,
"lr_schedule": {
":type:": "<class 'stable_baselines3.common.utils.FloatSchedule'>",
":serialized:": "gAWVeQAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMDUZsb2F0U2NoZWR1bGWUk5QpgZR9lIwOdmFsdWVfc2NoZWR1bGWUaACMEENvbnN0YW50U2NoZWR1bGWUk5QpgZR9lIwDdmFslEc/UGJN0vGp/HNic2Iu",
"value_schedule": "ConstantSchedule(val=0.001)"
},
"batch_norm_stats": [],
"batch_norm_stats_target": []
} |