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{
    "policy_class": {
        ":type:": "<class 'abc.ABCMeta'>",
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        "__module__": "stable_baselines3.sac.policies",
        "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
        "__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    ",
        "__init__": "<function SACPolicy.__init__ at 0x7fa5c6bd2200>",
        "_build": "<function SACPolicy._build at 0x7fa5c6bd2290>",
        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fa5c6bd2320>",
        "reset_noise": "<function SACPolicy.reset_noise at 0x7fa5c6bd23b0>",
        "make_actor": "<function SACPolicy.make_actor at 0x7fa5c6bd2440>",
        "make_critic": "<function SACPolicy.make_critic at 0x7fa5c6bd24d0>",
        "forward": "<function SACPolicy.forward at 0x7fa5c6bd2560>",
        "_predict": "<function SACPolicy._predict at 0x7fa5c6bd25f0>",
        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fa5c6bd2680>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fa5c6bd7840>"
    },
    "verbose": 1,
    "policy_kwargs": {
        "log_std_init": -3,
        "net_arch": [
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            300
        ],
        "use_sde": true
    },
    "num_timesteps": 1000000,
    "_total_timesteps": 1000000,
    "_num_timesteps_at_start": 0,
    "seed": 0,
    "action_noise": null,
    "start_time": 1671835216851530424,
    "learning_rate": {
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    "tensorboard_log": "runs/AntBulletEnv-v0__sac__3073263478__1671835214/AntBulletEnv-v0",
    "_last_obs": null,
    "_last_episode_starts": {
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    "sde_sample_freq": -1,
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    "_stats_window_size": 100,
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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    ",
        "__init__": "<function ReplayBuffer.__init__ at 0x7fa5c6b06440>",
        "add": "<function ReplayBuffer.add at 0x7fa5c6b064d0>",
        "sample": "<function ReplayBuffer.sample at 0x7fa5c6b06560>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fa5c6b065f0>",
        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7fa5c6b06680>)>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fa59b5364c0>"
    },
    "replay_buffer_kwargs": {},
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    "use_sde_at_warmup": false,
    "target_entropy": -8.0,
    "ent_coef": "auto",
    "target_update_interval": 1,
    "lr_schedule": {
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    "_action_repeat": [
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    ],
    "surgeon": null,
    "batch_norm_stats": [],
    "batch_norm_stats_target": [],
    "_last_action": {
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}