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{ |
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"policy_class": { |
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":type:": "<class 'abc.ABCMeta'>", |
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"__module__": "stable_baselines3.common.policies", |
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"__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 ", |
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f58260d2ee0>", |
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f58260d2f70>", |
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f58260d4040>", |
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f58260d40d0>", |
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"_build": "<function ActorCriticPolicy._build at 0x7f58260d4160>", |
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"forward": "<function ActorCriticPolicy.forward at 0x7f58260d41f0>", |
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f58260d4280>", |
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f58260d4310>", |
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"_predict": "<function ActorCriticPolicy._predict at 0x7f58260d43a0>", |
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f58260d4430>", |
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f58260d44c0>", |
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f58260d4550>", |
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"__abstractmethods__": "frozenset()", |
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"_abc_impl": "<_abc._abc_data object at 0x7f58264598c0>" |
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}, |
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"verbose": 1, |
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"policy_kwargs": { |
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":type:": "<class 'dict'>", |
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"log_std_init": -2, |
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"ortho_init": false, |
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"activation_fn": "<class 'torch.nn.modules.activation.ReLU'>", |
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"net_arch": { |
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"pi": [ |
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256 |
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], |
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"vf": [ |
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] |
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} |
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}, |
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"observation_space": { |
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":type:": "<class 'gym.spaces.box.Box'>", |
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"_shape": [ |
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}, |
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"n_steps": 512, |
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"gamma": 0.99, |
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"gae_lambda": 0.9, |
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"ent_coef": 3.62109e-06, |
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"vf_coef": 0.430793, |
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} |