{"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 0x7d4f9eed0ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d4f9eed0f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d4f9eed1000>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d4f9eed1090>", "_build": "<function ActorCriticPolicy._build at 0x7d4f9eed1120>", "forward": "<function ActorCriticPolicy.forward at 0x7d4f9eed11b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d4f9eed1240>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d4f9eed12d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d4f9eed1360>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d4f9eed13f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d4f9eed1480>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d4f9eed1510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d4f9ee73580>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712155503725871178, "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": -0.015808000000000044, "_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:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}