{"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 0x7c690538f130>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c690538f1c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c690538f250>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c690538f2e0>", "_build": "<function ActorCriticPolicy._build at 0x7c690538f370>", "forward": "<function ActorCriticPolicy.forward at 0x7c690538f400>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c690538f490>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c690538f520>", "_predict": "<function ActorCriticPolicy._predict at 0x7c690538f5b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c690538f640>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c690538f6d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c690538f760>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c690532e840>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1706970918929564700, "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": -0.007616000000000067, "_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": 492, "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-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": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}