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{
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.common.policies",
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\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()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\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 ",
"__init__": "<function ActorCriticPolicy.__init__ at 0x7fdc739f3950>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdc739f39e0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdc739f3a70>",
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdc739f3b00>",
"_build": "<function ActorCriticPolicy._build at 0x7fdc739f3b90>",
"forward": "<function ActorCriticPolicy.forward at 0x7fdc739f3c20>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdc739f3cb0>",
"_predict": "<function ActorCriticPolicy._predict at 0x7fdc739f3d40>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdc739f3dd0>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdc739f3e60>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdc739f3ef0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7fdc73a44840>"
},
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"policy_kwargs": {
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"eps": 1e-05,
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