{
    "policy_class": {
        ":type:": "<class 'abc.ABCMeta'>",
        ":serialized:": "gASVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=",
        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 feature 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_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    ",
        "__init__": "<function TQCPolicy.__init__ at 0x7f71b6f68710>",
        "_build": "<function TQCPolicy._build at 0x7f71b6f687a0>",
        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7f71b6f68830>",
        "reset_noise": "<function TQCPolicy.reset_noise at 0x7f71b6f688c0>",
        "make_actor": "<function TQCPolicy.make_actor at 0x7f71b6f68950>",
        "make_critic": "<function TQCPolicy.make_critic at 0x7f71b6f689e0>",
        "forward": "<function TQCPolicy.forward at 0x7f71b6f68a70>",
        "_predict": "<function TQCPolicy._predict at 0x7f71b6f68b00>",
        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7f71b6f68b90>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7f71b6fc8690>"
    },
    "verbose": 1,
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        "net_arch": [
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            300
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        "use_sde": true
    },
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        "dtype": "float32",
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        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf  1.]",
        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False  True]",
        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False  True]",
        "_np_random": null,
        "_shape": [
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    "action_space": {
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    "batch_size": 256,
    "learning_starts": 10000,
    "tau": 0.02,
    "gamma": 0.98,
    "gradient_steps": 64,
    "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:\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    :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 0x7f71b7742c20>",
        "sample": "<function ReplayBuffer.sample at 0x7f71b72a97a0>",
        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f71b72a9830>",
        "__abstractmethods__": "frozenset()",
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    "replay_buffer_kwargs": {},
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    "use_sde_at_warmup": false,
    "target_entropy": -8.0,
    "ent_coef": "auto",
    "target_update_interval": 1,
    "top_quantiles_to_drop_per_net": 2,
    "_last_dones": {
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    "remove_time_limit_termination": false
}