| {"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 0x7ab15755a3b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ab15755a440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ab15755a4d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ab15755a560>", "_build": "<function ActorCriticPolicy._build at 0x7ab15755a5f0>", "forward": "<function ActorCriticPolicy.forward at 0x7ab15755a680>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ab15755a710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ab15755a7a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ab15755a830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ab15755a8c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ab15755a950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ab15755a9e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ab1576f3000>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1735770083505726751, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAALPcVr2FS6A4divENNWpmzCkJwY8FYu8swAAgD8AAIA/zRTbuzbIKLw3Bw28XN2cPLCDiz3hNIG9AACAPwAAgD9Dn1G+ztyDP7inir6XHyG/SJWLvoVi6zwAAAAAAAAAAIAhvT1xjn4/osUCPql6sr4Wshw+JdznPQAAAAAAAAAA5rjTvT1KBLv40co7QocDPPSsoru99/I8AACAPwAAAACz0wq9VGWMvDe4xzsupY08Rfz7vbYYYz0AAIA/AACAPwDkCzzUtKg/cvvXvLwr877pnrc9AqssPgAAAAAAAAAA2lC0vQV7v7syCde8QUVfvvSuEbyO7d++AACAPwAAgD9teyO+qhq6PhgaYT1Jv52+ldMsvbIksz0AAAAAAAAAAGYj0zwALKY//rQePtuv475jl8095utRPgAAAAAAAAAAzSQdPTTonD0v+SY8tIRcviifU7veR4S8AAAAAAAAAABNi3+9g+dsvOqUWzuTQMg89xzTvVSynz0AAIA/AACAP0Clwr0Ub7w/ul0xv8EgTD2+ySa8iespvgAAAAAAAAAAM/sCvSlBS7yYMyU9pW0Mvg13QT3facg+AACAPwAAgD8A9Lm7NTRFPt4szTz4+oO+/U9oPNwJRr0AAAAAAAAAAM3nHb1IAYa6bq+WNavSMi67Mg67J+GwtAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.015808000000000044, "_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": 248, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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'>", ":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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |