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"__doc__": "\n Policy class (with both actor and critic) for SAC.\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 features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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_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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"__init__": "<function SACPolicy.__init__ at 0x7fe9a7611ca0>", |
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"reset_noise": "<function SACPolicy.reset_noise at 0x7fe9a7611e50>", |
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"make_actor": "<function SACPolicy.make_actor at 0x7fe9a7611ee0>", |
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"make_critic": "<function SACPolicy.make_critic at 0x7fe9a7611f70>", |
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"forward": "<function SACPolicy.forward at 0x7fe9a761a040>", |
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"_predict": "<function SACPolicy._predict at 0x7fe9a761a0d0>", |
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"set_training_mode": "<function SACPolicy.set_training_mode at 0x7fe9a761a160>", |
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"__abstractmethods__": "frozenset()", |
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":type:": "<class 'collections.deque'>", |
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"_n_updates": 19900, |
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"buffer_size": 1, |
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"batch_size": 256, |
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"learning_starts": 100, |
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"tau": 0.005, |
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"gamma": 0.99, |
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"gradient_steps": 1, |
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"optimize_memory_usage": false, |
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"replay_buffer_class": { |
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":type:": "<class 'abc.ABCMeta'>", |
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"__module__": "stable_baselines3.common.buffers", |
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\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 device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\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 ", |
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"__init__": "<function ReplayBuffer.__init__ at 0x7fe9a766a430>", |
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"add": "<function ReplayBuffer.add at 0x7fe9a766a4c0>", |
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"sample": "<function ReplayBuffer.sample at 0x7fe9a766a550>", |
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"_get_samples": "<function ReplayBuffer._get_samples at 0x7fe9a766a5e0>", |
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"__abstractmethods__": "frozenset()", |
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"_abc_impl": "<_abc._abc_data object at 0x7fe9a7660b80>" |
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}, |
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"replay_buffer_kwargs": {}, |
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"train_freq": { |
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":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", |
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"use_sde_at_warmup": false, |
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"target_entropy": -1.0, |
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"ent_coef": "auto", |
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"target_update_interval": 1, |
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"batch_norm_stats": [], |
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"batch_norm_stats_target": [] |
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} |