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":type:": "<class 'abc.ABCMeta'>", |
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"__module__": "stable_baselines3.common.policies", |
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"__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 ", |
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f6ac529c4c0>", |
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6ac529c550>", |
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6ac529c5e0>", |
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6ac529c670>", |
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"_build": "<function ActorCriticPolicy._build at 0x7f6ac529c700>", |
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"forward": "<function ActorCriticPolicy.forward at 0x7f6ac529c790>", |
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6ac529c820>", |
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"_predict": "<function ActorCriticPolicy._predict at 0x7f6ac529c8b0>", |
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6ac529c940>", |
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6ac529c9d0>", |
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6ac529ca60>", |
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"__abstractmethods__": "frozenset()", |
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"_abc_impl": "<_abc_data object at 0x7f6ac5289c90>" |
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}, |
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":serialized:": "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" |
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}, |
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"clip_range_vf": null, |
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"normalize_advantage": true, |
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"target_kl": null |
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} |