{"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 0x7efd312437f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efd31243880>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efd31243910>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efd312439a0>", "_build": "<function ActorCriticPolicy._build at 0x7efd31243a30>", "forward": "<function ActorCriticPolicy.forward at 0x7efd31243ac0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efd31243b50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efd31243be0>", "_predict": "<function ActorCriticPolicy._predict at 0x7efd31243c70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efd31243d00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efd31243d90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efd31243e20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efd31245140>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688207218837842336, "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:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": 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-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"}}