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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 0x788a8d53ba30>", |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x788a8d53bac0>", |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x788a8d53bb50>", |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x788a8d53bbe0>", |
| "_build": "<function ActorCriticPolicy._build at 0x788a8d53bc70>", |
| "forward": "<function ActorCriticPolicy.forward at 0x788a8d53bd00>", |
| "extract_features": "<function ActorCriticPolicy.extract_features at 0x788a8d53bd90>", |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x788a8d53be20>", |
| "_predict": "<function ActorCriticPolicy._predict at 0x788a8d53beb0>", |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x788a8d53bf40>", |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x788a8d548040>", |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x788a8d5480d0>", |
| "__abstractmethods__": "frozenset()", |
| "_abc_impl": "<_abc._abc_data object at 0x788a8d5410c0>" |
| }, |
| "verbose": 1, |
| "policy_kwargs": {}, |
| "num_timesteps": 1000448, |
| "_total_timesteps": 1000000, |
| "_num_timesteps_at_start": 0, |
| "seed": null, |
| "action_noise": null, |
| "start_time": 1717944440915434578, |
| "learning_rate": 0.0003, |
| "tensorboard_log": null, |
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