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{
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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7ff9b85e1240>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff9b85e12d0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff9b85e1360>",
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"forward": "<function ActorCriticPolicy.forward at 0x7ff9b85e1510>",
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff9b85e15a0>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7ff9b85e16c0>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff9b85e1750>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff9b85e17e0>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff9b85e1870>",
"__abstractmethods__": "frozenset()",
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},
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"use_sde": false,
"sde_sample_freq": -1,
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"_n_updates": 24416,
"n_steps": 1024,
"gamma": 0.999,
"gae_lambda": 0.98,
"ent_coef": 0.01,
"vf_coef": 0.5,
"max_grad_norm": 0.5,
"batch_size": 64,
"n_epochs": 4,
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