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        "__module__": "stable_baselines3.sac.policies",
        "__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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        "reset_noise": "<function SACPolicy.reset_noise at 0x7fe57c9d1e50>",
        "make_actor": "<function SACPolicy.make_actor at 0x7fe57c9d1ee0>",
        "make_critic": "<function SACPolicy.make_critic at 0x7fe57c9d1f70>",
        "forward": "<function SACPolicy.forward at 0x7fe57c9da040>",
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        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fe57c9da160>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fe57c9d9500>"
    },
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        "bounded_above": "[ True  True  True]",
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    "_n_updates": 19900,
    "buffer_size": 1,
    "batch_size": 256,
    "learning_starts": 100,
    "tau": 0.005,
    "gamma": 0.99,
    "gradient_steps": 1,
    "optimize_memory_usage": false,
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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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        "add": "<function ReplayBuffer.add at 0x7fe57ca294c0>",
        "sample": "<function ReplayBuffer.sample at 0x7fe57ca29550>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fe57ca295e0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fe57ca2a180>"
    },
    "replay_buffer_kwargs": {},
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    "use_sde_at_warmup": false,
    "target_entropy": -1.0,
    "ent_coef": "auto",
    "target_update_interval": 1,
    "batch_norm_stats": [],
    "batch_norm_stats_target": []
}