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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 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    ",
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        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7f9908ee35e0>",
        "reset_noise": "<function TQCPolicy.reset_noise at 0x7f9908ee3670>",
        "make_actor": "<function TQCPolicy.make_actor at 0x7f9908ee3700>",
        "make_critic": "<function TQCPolicy.make_critic at 0x7f9908ee3790>",
        "forward": "<function TQCPolicy.forward at 0x7f9908ee3820>",
        "_predict": "<function TQCPolicy._predict at 0x7f9908ee38b0>",
        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7f9908ee3940>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f9908ee4080>"
    },
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        "bounded_above": "[ True  True  True]",
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    "ep_success_buffer": {
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    "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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        "__init__": "<function ReplayBuffer.__init__ at 0x7f990936a430>",
        "add": "<function ReplayBuffer.add at 0x7f990936a4c0>",
        "sample": "<function ReplayBuffer.sample at 0x7f990936a550>",
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        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7f99093691c0>"
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    "replay_buffer_kwargs": {},
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    "use_sde_at_warmup": false,
    "target_entropy": -1.0,
    "ent_coef": "auto",
    "target_update_interval": 1,
    "top_quantiles_to_drop_per_net": 2,
    "batch_norm_stats": [],
    "batch_norm_stats_target": []
}