| { | |
| "policy_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
| ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", | |
| "__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 0x7f112bc50d30>", | |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f112bc50dc0>", | |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f112bc50e50>", | |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f112bc50ee0>", | |
| "_build": "<function ActorCriticPolicy._build at 0x7f112bc50f70>", | |
| "forward": "<function ActorCriticPolicy.forward at 0x7f112bc52040>", | |
| "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f112bc520d0>", | |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f112bc52160>", | |
| "_predict": "<function ActorCriticPolicy._predict at 0x7f112bc521f0>", | |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f112bc52280>", | |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f112bc52310>", | |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f112bc523a0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f112bc4e980>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| ":type:": "<class 'dict'>", | |
| ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", | |
| "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'>", | |
| ":serialized:": "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", | |
| "dtype": "float32", | |
| "_shape": [ | |
| 6 | |
| ], | |
| "low": "[ -1. -1. -1. -1. -12.566371 -28.274334]", | |
| "high": "[ 1. 1. 1. 1. 12.566371 28.274334]", | |
| "bounded_below": "[ True True True True True True]", | |
| "bounded_above": "[ True True True True True True]", | |
| "_np_random": null | |
| }, | |
| "action_space": { | |
| ":type:": "<class 'gym.spaces.discrete.Discrete'>", | |
| ":serialized:": "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", | |
| "n": 3, | |
| "_shape": [], | |
| "dtype": "int64", | |
| "_np_random": "RandomState(MT19937)" | |
| }, | |
| "n_envs": 1, | |
| "num_timesteps": 500000, | |
| "_total_timesteps": 500000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": 0, | |
| "action_noise": null, | |
| "start_time": 1670933474141942728, | |
| "learning_rate": 0.0007, | |
| "tensorboard_log": "runs/Acrobot-v1__a2c__2453577583__1670933471/Acrobot-v1", | |
| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
| ":serialized:": "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" | |
| }, | |
| "_last_obs": null, | |
| "_last_episode_starts": { | |
| ":type:": "<class 'numpy.ndarray'>", | |
| ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAQAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg==" | |
| }, | |
| "_last_original_obs": { | |
| ":type:": "<class 'numpy.ndarray'>", | |
| ":serialized:": "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" | |
| }, | |
| "_episode_num": 0, | |
| "use_sde": false, | |
| "sde_sample_freq": -1, | |
| "_current_progress_remaining": 0.0, | |
| "ep_info_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "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" | |
| }, | |
| "ep_success_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg==" | |
| }, | |
| "_n_updates": 6250, | |
| "n_steps": 5, | |
| "gamma": 0.99, | |
| "gae_lambda": 1.0, | |
| "ent_coef": 0.0, | |
| "vf_coef": 0.5, | |
| "max_grad_norm": 0.5, | |
| "normalize_advantage": false | |
| } |