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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 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 0x7fcdc5d53ee0>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcdc5d53f70>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcdc5d54040>",
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"forward": "<function ActorCriticPolicy.forward at 0x7fcdc5d541f0>",
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcdc5d54280>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcdc5d54310>",
"_predict": "<function ActorCriticPolicy._predict at 0x7fcdc5d543a0>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcdc5d54430>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcdc5d544c0>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcdc5d54550>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7fcdc617cf00>"
},
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"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]",
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"n_steps": 1024,
"gamma": 0.99,
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"ent_coef": 0.0,
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"batch_size": 128,
"cg_max_steps": 25,
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"line_search_shrinking_factor": 0.8,
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"target_kl": 0.01,
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} |