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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 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 0x7f3470a7cdd0>", | |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3470a7ce60>", | |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3470a7cef0>", | |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3470a7cf80>", | |
| "_build": "<function ActorCriticPolicy._build at 0x7f3470a86050>", | |
| "forward": "<function ActorCriticPolicy.forward at 0x7f3470a860e0>", | |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3470a86170>", | |
| "_predict": "<function ActorCriticPolicy._predict at 0x7f3470a86200>", | |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3470a86290>", | |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3470a86320>", | |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3470a863b0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f3470ad6420>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| ":type:": "<class 'dict'>", | |
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| "activation_fn": "<class 'torch.nn.modules.activation.SELU'>", | |
| "net_arch": [ | |
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| "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", | |
| "high": "[inf inf inf inf inf inf inf inf]", | |
| "bounded_below": "[False False False False False False False False]", | |
| "bounded_above": "[False False False False False False False False]", | |
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| "n_envs": 32, | |
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| "_total_timesteps": 200000, | |
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| "seed": null, | |
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| "learning_rate": 0.0003, | |
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| }, | |
| "ep_success_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
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| }, | |
| "_n_updates": 288, | |
| "n_steps": 1048, | |
| "gamma": 0.999, | |
| "gae_lambda": 0.98, | |
| "ent_coef": 0.01, | |
| "vf_coef": 0.5, | |
| "max_grad_norm": 0.5, | |
| "batch_size": 64, | |
| "n_epochs": 4, | |
| "clip_range": { | |
| ":type:": "<class 'function'>", | |
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| }, | |
| "clip_range_vf": null, | |
| "normalize_advantage": true, | |
| "target_kl": null | |
| } |