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| "batch_size": 256, | |
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| "__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 ", | |
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| "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7d585e14a290>", | |
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| "sample": "<function HerReplayBuffer.sample at 0x7d585e14a560>", | |
| "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x7d585e14a5f0>", | |
| "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x7d585e14a680>", | |
| "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x7d585e14a710>", | |
| "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7d585e14a7a0>", | |
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| "goal_selection_strategy": "GoalSelectionStrategy.FUTURE" | |
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| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
| ":serialized:": "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" | |
| }, | |
| "actor_batch_norm_stats": [], | |
| "critic_batch_norm_stats": [], | |
| "actor_batch_norm_stats_target": [], | |
| "critic_batch_norm_stats_target": [] | |
| } |