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{
"policy_class": {
":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 0x7f34c2946950>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f34c29469e0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f34c2946a70>",
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f34c2946b00>",
"_build": "<function ActorCriticPolicy._build at 0x7f34c2946b90>",
"forward": "<function ActorCriticPolicy.forward at 0x7f34c2946c20>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f34c2946cb0>",
"_predict": "<function ActorCriticPolicy._predict at 0x7f34c2946d40>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f34c2946dd0>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f34c2946e60>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f34c2946ef0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f34c2998840>"
},
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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"eps": 1e-05,
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"n_steps": 5,
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"gae_lambda": 1.0,
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"vf_coef": 0.5,
"max_grad_norm": 0.5,
"normalize_advantage": false
} |