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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 0x7f18b7a81320>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f18b7a813b0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f18b7a81440>",
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f18b7a814d0>",
"_build": "<function ActorCriticPolicy._build at 0x7f18b7a81560>",
"forward": "<function ActorCriticPolicy.forward at 0x7f18b7a815f0>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f18b7a81680>",
"_predict": "<function ActorCriticPolicy._predict at 0x7f18b7a81710>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f18b7a817a0>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f18b7a81830>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f18b7a818c0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f18b7a46ba0>"
},
"verbose": 1,
"policy_kwargs": {},
"observation_space": {
":type:": "<class 'gym.spaces.box.Box'>",
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"dtype": "float32",
"_shape": [
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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]",
"_np_random": null
},
"action_space": {
":type:": "<class 'gym.spaces.discrete.Discrete'>",
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"n": 4,
"_shape": [],
"dtype": "int64",
"_np_random": null
},
"n_envs": 16,
"num_timesteps": 0,
"_total_timesteps": 0,
"_num_timesteps_at_start": 0,
"seed": null,
"action_noise": null,
"start_time": null,
"learning_rate": 0.0003,
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"lr_schedule": {
":type:": "<class 'function'>",
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},
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"_last_episode_starts": null,
"_last_original_obs": null,
"_episode_num": 0,
"use_sde": false,
"sde_sample_freq": -1,
"_current_progress_remaining": 1,
"ep_info_buffer": null,
"ep_success_buffer": null,
"_n_updates": 0,
"n_steps": 1024,
"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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"target_kl": null
} |