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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 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 ", |
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f689532bd30>", |
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f689532bdc0>", |
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f689532be50>", |
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f689532bee0>", |
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"_build": "<function ActorCriticPolicy._build at 0x7f689532bf70>", |
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"forward": "<function ActorCriticPolicy.forward at 0x7f689532f040>", |
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f689532f0d0>", |
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f689532f160>", |
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"_predict": "<function ActorCriticPolicy._predict at 0x7f689532f1f0>", |
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f689532f280>", |
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f689532f310>", |
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f689532f3a0>", |
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"__abstractmethods__": "frozenset()", |
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}, |
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":serialized:": "gAWVxAEAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUtDQwRkAVMAlE5HAAAAAAAAAACGlCmMAV+UhZSMHzxpcHl0aG9uLWlucHV0LTQ1LWU4MmUyMDdjOWFjNT6UjAg8bGFtYmRhPpRLDUMAlCkpdJRSlH2UKIwLX19wYWNrYWdlX1+UTowIX19uYW1lX1+UjAhfX21haW5fX5R1Tk5OdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgWfZR9lChoE2gNjAxfX3F1YWxuYW1lX1+UaA2MD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBSMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UTowXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg==" |
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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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} |