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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 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 0x7c250803b490>", |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c250803b520>", |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c250803b5b0>", |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c250803b640>", |
| "_build": "<function ActorCriticPolicy._build at 0x7c250803b6d0>", |
| "forward": "<function ActorCriticPolicy.forward at 0x7c250803b760>", |
| "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c250803b7f0>", |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c250803b880>", |
| "_predict": "<function ActorCriticPolicy._predict at 0x7c250803b910>", |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c250803b9a0>", |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c250803ba30>", |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c250803bac0>", |
| "__abstractmethods__": "frozenset()", |
| "_abc_impl": "<_abc._abc_data object at 0x7c250803e100>" |
| }, |
| "verbose": 1, |
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| "learning_rate": 0.0003, |
| "tensorboard_log": null, |
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| "lr_schedule": { |
| ":type:": "<class 'function'>", |
| ":serialized:": "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" |
| } |
| } |
