| { | |
| "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 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 0x7f74713728c0>", | |
| "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7471372950>", | |
| "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74713729e0>", | |
| "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7471372a70>", | |
| "_build": "<function ActorCriticPolicy._build at 0x7f7471372b00>", | |
| "forward": "<function ActorCriticPolicy.forward at 0x7f7471372b90>", | |
| "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7471372c20>", | |
| "_predict": "<function ActorCriticPolicy._predict at 0x7f7471372cb0>", | |
| "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7471372d40>", | |
| "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7471372dd0>", | |
| "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7471372e60>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f7471349390>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": {}, | |
| "observation_space": { | |
| ":type:": "<class 'gym.spaces.box.Box'>", | |
| ":serialized:": "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", | |
| "dtype": "float32", | |
| "_shape": [ | |
| 3 | |
| ], | |
| "low": "[-1. -1. -8.]", | |
| "high": "[1. 1. 8.]", | |
| "bounded_below": "[ True True True]", | |
| "bounded_above": "[ True True True]", | |
| "_np_random": null | |
| }, | |
| "action_space": { | |
| ":type:": "<class 'gym.spaces.box.Box'>", | |
| ":serialized:": "gAWVWQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLAYWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAAAAADAlGgKSwGFlIwBQ5R0lFKUjARoaWdolGgSKJYEAAAAAAAAAAAAAECUaApLAYWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYBAAAAAAAAAAGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLAYWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYBAAAAAAAAAAGUaCFLAYWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", | |
| "dtype": "float32", | |
| "_shape": [ | |
| 1 | |
| ], | |
| "low": "[-2.]", | |
| "high": "[2.]", | |
| "bounded_below": "[ True]", | |
| "bounded_above": "[ True]", | |
| "_np_random": null | |
| }, | |
| "n_envs": 1, | |
| "num_timesteps": 100352, | |
| "_total_timesteps": 100000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": null, | |
| "action_noise": null, | |
| "start_time": 1651656807.660387, | |
| "learning_rate": 0.001, | |
| "tensorboard_log": null, | |
| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
| ":serialized:": "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" | |
| }, | |
| "_last_obs": { | |
| ":type:": "<class 'numpy.ndarray'>", | |
| ":serialized:": "gAWVgQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAHnwfz9RULK8EVDfvZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsDhpSMAUOUdJRSlC4=" | |
| }, | |
| "_last_episode_starts": { | |
| ":type:": "<class 'numpy.ndarray'>", | |
| ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg==" | |
| }, | |
| "_last_original_obs": null, | |
| "_episode_num": 0, | |
| "use_sde": true, | |
| "sde_sample_freq": 4, | |
| "_current_progress_remaining": -0.0035199999999999676, | |
| "ep_info_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "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" | |
| }, | |
| "ep_success_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg==" | |
| }, | |
| "_n_updates": 490, | |
| "n_steps": 2048, | |
| "gamma": 0.98, | |
| "gae_lambda": 0.95, | |
| "ent_coef": 0.0, | |
| "vf_coef": 0.5, | |
| "max_grad_norm": 0.5, | |
| "batch_size": 64, | |
| "n_epochs": 10, | |
| "clip_range": { | |
| ":type:": "<class 'function'>", | |
| ":serialized:": "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" | |
| }, | |
| "clip_range_vf": null, | |
| "normalize_advantage": true, | |
| "target_kl": null | |
| } |