| {"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 0x7f54fa224ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f54fa224f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f54fa225000>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f54fa225090>", "_build": "<function ActorCriticPolicy._build at 0x7f54fa225120>", "forward": "<function ActorCriticPolicy.forward at 0x7f54fa2251b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f54fa225240>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f54fa2252d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f54fa225360>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f54fa2253f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f54fa225480>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f54fa225510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f54fa2213c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 229376, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1687458503176755848, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.1468799999999999, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 210, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |