| { | |
| "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 0x7f87ba96bef0>", | |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f87ba96bf80>", | |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f87ba973050>", | |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f87ba9730e0>", | |
| "_build": "<function ActorCriticPolicy._build at 0x7f87ba973170>", | |
| "forward": "<function ActorCriticPolicy.forward at 0x7f87ba973200>", | |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f87ba973290>", | |
| "_predict": "<function ActorCriticPolicy._predict at 0x7f87ba973320>", | |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f87ba9733b0>", | |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f87ba973440>", | |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f87ba9734d0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f87ba9aaf90>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| ":type:": "<class 'dict'>", | |
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| "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", | |
| "optimizer_kwargs": { | |
| "alpha": 0.99, | |
| "eps": 1e-05, | |
| "weight_decay": 0 | |
| } | |
| }, | |
| "observation_space": { | |
| ":type:": "<class 'gym.spaces.box.Box'>", | |
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| "dtype": "float32", | |
| "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]", | |
| "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]", | |
| "bounded_below": "[ True True True True]", | |
| "bounded_above": "[ True True True True]", | |
| "_np_random": null, | |
| "_shape": [ | |
| 4 | |
| ] | |
| }, | |
| "action_space": { | |
| ":type:": "<class 'gym.spaces.discrete.Discrete'>", | |
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| "n": 2, | |
| "dtype": "int64", | |
| "_np_random": "RandomState(MT19937)", | |
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| }, | |
| "n_envs": 8, | |
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| "_total_timesteps": 500000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": 0, | |
| "action_noise": null, | |
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| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
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