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| "__module__": "sb3_contrib.tqc.policies", | |
| "__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", | |
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| 512, | |
| 512 | |
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| "use_sde": false | |
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| "bounded_above": "[ True True True True]", | |
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| "high_repr": "1.0", | |
| "_np_random": "Generator(PCG64)" | |
| }, | |
| "n_envs": 1, | |
| "buffer_size": 1000000, | |
| "batch_size": 2048, | |
| "learning_starts": 100, | |
| "tau": 0.05, | |
| "gamma": 0.95, | |
| "gradient_steps": 1, | |
| "optimize_memory_usage": false, | |
| "replay_buffer_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
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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 0x7f75dba7ce50>", | |
| "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7f75dba7cee0>", | |
| "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7f75dba7cf70>", | |
| "set_env": "<function HerReplayBuffer.set_env at 0x7f75dba7d000>", | |
| "add": "<function HerReplayBuffer.add at 0x7f75dba7d090>", | |
| "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x7f75dba7d120>", | |
| "sample": "<function HerReplayBuffer.sample at 0x7f75dba7d1b0>", | |
| "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x7f75dba7d240>", | |
| "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x7f75dba7d2d0>", | |
| "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x7f75dba7d360>", | |
| "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f75dba7d3f0>", | |
| "__abstractmethods__": "frozenset()", | |
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| }, | |
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| "goal_selection_strategy": "future" | |
| }, | |
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| }, | |
| "use_sde_at_warmup": false, | |
| "target_entropy": -4.0, | |
| "ent_coef": "auto", | |
| "target_update_interval": 1, | |
| "top_quantiles_to_drop_per_net": 2, | |
| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
| ":serialized:": "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" | |
| }, | |
| "batch_norm_stats": [], | |
| "batch_norm_stats_target": [] | |
| } |