{"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 0x7eb4b6094040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eb4b60940d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eb4b6094160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eb4b60941f0>", "_build": "<function ActorCriticPolicy._build at 0x7eb4b6094280>", "forward": "<function ActorCriticPolicy.forward at 0x7eb4b6094310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eb4b60943a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eb4b6094430>", "_predict": "<function ActorCriticPolicy._predict at 0x7eb4b60944c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eb4b6094550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eb4b60945e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eb4b6094670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eb4b61fb8c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1728024059267612722, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAALOSNb1qoKw/VSN0vsMMmL7Zy2+9soysvQAAAAAAAAAAmgqTPfYwXbqM5Um7pK6vthbNFrs5Kx42AACAPwAAgD8aJNm9j84vutkNgDvQz544PvWNO27r0LkAAIA/AACAPwY2Cr48cBA/Uf2CPgF/iL5ZZQQ+SPrEPAAAAAAAAAAAuihBPkEuAz++ooS9hVo9vl+BNb29SNQ9AAAAAAAAAADNiD494WCTujX/oLrJtfQ1kwakOhQwujkAAIA/AACAP+Ybur2Fq/y5EwBPurGVh7UCT9s7ZnZ3OQAAgD8AAIA/Zhw9vf1j2T4gQvg8NOtevhQyBz2/EDi9AAAAAAAAAACAZCa9XMNUusAysju84402D0guOoMY0roAAIA/AACAP00JcT1IUY26Q8+IuyMaizgKpi8785VxOQAAgD8AAIA/zbhePVwjPbolF0C7AOubtDfQIzpohmM6AACAPwAAgD+Ap769w0lSuvsejDq3dRE1WXiYur7zpLkAAIA/AACAP00WVT0N0wY/sZKuvHwuVL44ZCU9E2vnvAAAAAAAAAAATa9YPS88cT615yC+EBEavmKB97wGSoK8AAAAAAAAAAAzhUy9FAy2unffFToeBv40/bDsuW8JK7kAAIA/AACAPwBEgLvYDIk+bWGqvd5HYb5CVW690LoovQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}