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| ":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 0x7f2116064560>", | |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f21160645f0>", | |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2116064680>", | |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2116064710>", | |
| "_build": "<function ActorCriticPolicy._build at 0x7f21160647a0>", | |
| "forward": "<function ActorCriticPolicy.forward at 0x7f2116064830>", | |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f21160648c0>", | |
| "_predict": "<function ActorCriticPolicy._predict at 0x7f2116064950>", | |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f21160649e0>", | |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2116064a70>", | |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2116064b00>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f21160abba0>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": {}, | |
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| }, | |
| "n_envs": 16, | |
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| "_total_timesteps": 500000, | |
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| "seed": null, | |
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| "learning_rate": 0.0003, | |
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