{"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 0x7c91789044c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c9178904550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c91789045e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c9178904670>", "_build": "<function ActorCriticPolicy._build at 0x7c9178904700>", "forward": "<function ActorCriticPolicy.forward at 0x7c9178904790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c9178904820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c91789048b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c9178904940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c91789049d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c9178904a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c9178904af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c9196ac96c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1705483942659272428, "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:": "gAWVMgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGxyUvwmVqyMAWyUTSIBjAF0lEdAkMyGrXDm83V9lChoBkdAcVE3GGVRk2gHTRgBaAhHQJDNNopQUHp1fZQoaAZHQHMH7s0HhS9oB00TAWgIR0CQzVJF9a2XdX2UKGgGR0ByLwJkXk5qaAdNCgFoCEdAkM3FRP420nV9lChoBkdAb82AtnPE9GgHTSEBaAhHQJDPCg6EJ0J1fZQoaAZHQGyxMH0K7ZpoB00aAWgIR0CQ0AFBppN9dX2UKGgGR0BxZQg4ffXPaAdNHwFoCEdAkNBwz1schnV9lChoBkdAcCkEZiuuBGgHTR4BaAhHQJDQe04R28t1fZQoaAZHQHBd08aGYa5oB00PAWgIR0CQ0lNBnjABdX2UKGgGR0BxqW0Re1KHaAdNEAFoCEdAkNLG6f8Mu3V9lChoBkdAcQfCsfaHsWgHS/toCEdAkNMNrTH80nV9lChoBkdAcMmFcpsoD2gHTSABaAhHQJDTIngHeJp1fZQoaAZHQGOmaAFxGUhoB03oA2gIR0CQ1AtUXHindX2UKGgGR0BxEV2hZha1aAdNNQFoCEdAkNS9FnZkCnV9lChoBkdAcYvIIF/x2GgHTQ0BaAhHQJDVElme18d1fZQoaAZHQHB/cCDEm6ZoB00xAWgIR0CQ1SvpQk5ZdX2UKGgGR0ByuuvicXnAaAdNEgFoCEdAkNXoX9BKMHV9lChoBkdAbefGza9K3GgHTQkBaAhHQJDWGQQtjCp1fZQoaAZHQHDdjf779AJoB005AWgIR0CQ1vO5avA5dX2UKGgGR0BvMaoddVvNaAdNLwFoCEdAkNiPwRXfZXV9lChoBkdAcKhsBhhH9WgHTRABaAhHQJDY98E3bVV1fZQoaAZHQHHUMBp5/spoB00pAWgIR0CQ2bKISDh+dX2UKGgGR0BuRGgBcRlIaAdNNwFoCEdAkNm2Ya5wwXV9lChoBkdAc2Pz8gpz92gHS+loCEdAkNn6vmoze3V9lChoBkdAbe9HT7VJ+WgHS/9oCEdAkNo+SSvC/HV9lChoBkdAbtwFJxvNvGgHTQYBaAhHQJDa/yrgflp1fZQoaAZHQEQ9WaMJhORoB0vUaAhHQJDbFd6cAip1fZQoaAZHQG6ED7Q9ic5oB005AWgIR0CQ3I6K+BYndX2UKGgGR0ByINHf/FR6aAdL9mgIR0CQ3UUtI066dX2UKGgGR0Byd50PpY9xaAdNQAFoCEdAkN2hGtp22XV9lChoBkdAcKV4ecQRPGgHTSYBaAhHQJDd65hBqsV1fZQoaAZHQHAoThky1u1oB00+AWgIR0CQ3olchTwVdX2UKGgGR0BlE9R3u/lAaAdN6ANoCEdAkN6KRyOrAHV9lChoBkdAcJO+8Gs3hmgHTSYBaAhHQJDe4QGwA2h1fZQoaAZHQHG5ngDRtxdoB00bAWgIR0CQ32B9Cu2adX2UKGgGR0BxqdHQQcxTaAdL+GgIR0CQ38KTB68hdX2UKGgGR0BwXsewLVnVaAdNAAFoCEdAkOBaRhc7hnV9lChoBkdAcuXxwQ176mgHTQcBaAhHQJDhdwaR6nl1fZQoaAZHQHGoEFwDNhVoB00fAWgIR0CQ4eWcz67/dX2UKGgGR0BsyOOlwcYJaAdNJQFoCEdAkOIRy4nWrnV9lChoBkdAbswEHMUypWgHTREBaAhHQJD2M6RyOrB1fZQoaAZHQHFWuc2BJ7NoB00uAWgIR0CQ9kAVfu1GdX2UKGgGR0BvwnyNGViXaAdNBAFoCEdAkPdUq2Bre3V9lChoBkdAcEz2uxKQJWgHTUUBaAhHQJD3vl5nlGR1fZQoaAZHQHHY5ssQNCtoB0v0aAhHQJD34nLJSzh1fZQoaAZHQHKcAl4TsY5oB00VAWgIR0CQ+Spxm03PdX2UKGgGR0ByWAQbuMMraAdNKgFoCEdAkPkp3gUDdXV9lChoBkdAcfjsbedkKGgHTQ8BaAhHQJD5oC4jKPp1fZQoaAZHQG+Hrmhdt2toB00PAWgIR0CQ+grd30PIdX2UKGgGR0BzBtSBK+SKaAdNGwFoCEdAkPoJrcj7h3V9lChoBkdAcVO/FirksGgHS/hoCEdAkPpcQZn+Q3V9lChoBkdAcKEzhgmZ3WgHTSkBaAhHQJD7SWldkax1fZQoaAZHQHE1+ObRWtFoB00DAWgIR0CQ/GH2h7E6dX2UKGgGR0Bun2HP/rB1aAdNNgFoCEdAkPyxwyZa3nV9lChoBkdAcQdBUaQ3gmgHTRMBaAhHQJD9bwCr92p1fZQoaAZHQG6AJobn5i5oB0v8aAhHQJD9kd6sySF1fZQoaAZHQHG/ZQ+EAYJoB01EAWgIR0CQ/qRISUTtdX2UKGgGR0BxX6PvKEFoaAdNAAFoCEdAkP7FEy+HrXV9lChoBkdAcgNZ13dKumgHS+9oCEdAkP7V+I/JNnV9lChoBkdAcrXJXyRSxmgHTSwBaAhHQJD+3O8kD6p1fZQoaAZHQHAp/FNtZV5oB00WAWgIR0CQ/74wRGtqdX2UKGgGR0BzWcddVvMsaAdNAQFoCEdAkQB14LThHnV9lChoBkdAUICVzIV/MGgHS+loCEdAkQCN4NZvDXV9lChoBkdAcx+Net0V8GgHS/doCEdAkQCZP2wmmnV9lChoBkdAcKGFOwgTy2gHTR4BaAhHQJEBPL1VYIV1fZQoaAZHQG82GRFI/aBoB00kAWgIR0CRAjhtLteEdX2UKGgGR0BwQW5mRNh3aAdNOwFoCEdAkQM6FdszmHV9lChoBkdAb32KRdQfp2gHTQQBaAhHQJEDutyPuG91fZQoaAZHQG54RE4Nqg1oB00tAWgIR0CRA8vBacI7dX2UKGgGR0ByCNzKcNH6aAdL/2gIR0CRA+RPGhmHdX2UKGgGR0Byl3p5eJHiaAdNAQFoCEdAkQSV5GBnSXV9lChoBkdAcKEfKZDzAmgHTSUBaAhHQJEFqgRK6Fx1fZQoaAZHQHItq5oXbdtoB00XAWgIR0CRBpkN4JNTdX2UKGgGR0BzFRqagElmaAdNKAFoCEdAkQbkH2RJVnV9lChoBkdAcHgMOwxFiWgHTSgBaAhHQJEHA21lXil1fZQoaAZHQHHIqe9SMtNoB00tAWgIR0CRBzc5sCT2dX2UKGgGR0ByGK02LpA2aAdNIwFoCEdAkQfkpmVZ93V9lChoBkdAcuFiKBNEgGgHTSEBaAhHQJEIjMY/FBJ1fZQoaAZHQHDMYT4+KTBoB00hAWgIR0CRCKVymygPdX2UKGgGR0BxHRnAZbY9aAdNFgFoCEdAkQkMRcu8LHV9lChoBkdAcJvoLG7z1GgHTTgBaAhHQJEJWXRgJC11fZQoaAZHQHHsc8TzundoB00kAWgIR0CRCk+QU5+6dX2UKGgGR0Bx1J2X9itraAdL92gIR0CRCqrv9cbBdX2UKGgGR0BwLObExZdOaAdNCQFoCEdAkQsYRZlnRXV9lChoBkdAbXHP8AJb+2gHTSQBaAhHQJELy3Td+G51fZQoaAZHQG/MXazu4PRoB00YAWgIR0CRDGnYQJ5WdX2UKGgGR0BvMyGahHskaAdNSwFoCEdAkQxr8m8dxXV9lChoBkdAcCmiY9gWrWgHS/JoCEdAkQ1roOhCdHV9lChoBkdAcKMd8Aq/d2gHS/hoCEdAkQ3a/ub7THV9lChoBkdAbUdaSLZSN2gHTQcBaAhHQJEOn0AcT8J1fZQoaAZHQG9T1sDW9UVoB01GAWgIR0CRDuhw2l2vdX2UKGgGR0BxfWEEkjX4aAdNIQFoCEdAkQ8n7k4m1XV9lChoBkdAbXfZFocrAmgHTRABaAhHQJEQRn8Kohp1fZQoaAZHQHBFV8Ti84BoB00wAWgIR0CREIcLSeAedX2UKGgGR0Bx9bPC2tuDaAdL/2gIR0CREJww0wajdX2UKGgGR0BxweGVRk3CaAdNLAFoCEdAkRGyswL3K3V9lChoBkdAcTeUT+NtImgHTU0BaAhHQJESOPvKEFp1fZQoaAZHQHM6/LTx5LRoB0vVaAhHQJESoq9XcQB1fZQoaAZHQHK6M9r433poB00YAWgIR0CREtEKmbb2dX2UKGgGR0BvnCPluFYdaAdNKwFoCEdAkRL/zWf9P3VlLg=="}, "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:": "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"}}