{"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 0x7a8b19af9d80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a8b19af9e10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a8b19af9ea0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a8b19af9f30>", "_build": "<function ActorCriticPolicy._build at 0x7a8b19af9fc0>", "forward": "<function ActorCriticPolicy.forward at 0x7a8b19afa050>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a8b19afa0e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a8b19afa170>", "_predict": "<function ActorCriticPolicy._predict at 0x7a8b19afa200>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a8b19afa290>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a8b19afa320>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a8b19afa3b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a8b19b08680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710916391373728241, "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:": "gAWV8wsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHMEeV5a/yqMAWyUS+6MAXSUR0CXY5NMXaakdX2UKGgGR0Bwf9n7HhjwaAdL/mgIR0CXZPotL+PzdX2UKGgGR0BxfVceKbazaAdNGgFoCEdAl2VCTyJ9A3V9lChoBkdAb8CMgEEDAGgHS8RoCEdAl2VwuM+/xnV9lChoBkdAcBfvKlpGnWgHS8poCEdAl2WBeHBUJnV9lChoBkdAcH1hw2l2vGgHS/9oCEdAl2XBxkupTHV9lChoBkdAbPQ482aUimgHS95oCEdAl2XVJL/S6XV9lChoBkdAch/0U47zTWgHS99oCEdAl2Zr4agmJHV9lChoBkdAcDzdQfp2U2gHS/JoCEdAl2aC+Yc/+3V9lChoBkdAcMquGbkOqmgHS/JoCEdAl2dl0YCQtHV9lChoBkdAbag45Lh73WgHS8xoCEdAl2hB4Y77sXV9lChoBkdAcI4aisXBQGgHS99oCEdAl2iWoaUA1nV9lChoBkdARjbi83++/WgHS8VoCEdAl2ig7HQyAXV9lChoBkdAcEGCXhOxjmgHTSEBaAhHQJdrAOAiFCd1fZQoaAZHQHGEpqVQhwFoB0vTaAhHQJds+X2M85l1fZQoaAZHQG+nqmj0tiBoB0vkaAhHQJdtEwRGtp51fZQoaAZHQHA8DBuXNTtoB0vmaAhHQJdtkx0uDjB1fZQoaAZHQG/zYTbnHNpoB0vlaAhHQJdt3ZuhsZZ1fZQoaAZHQHC8NZid8RdoB0voaAhHQJdugJIDoyN1fZQoaAZHQHDWsHB1s+FoB0vYaAhHQJdu0kAxSHd1fZQoaAZHQG9ebDdgv11oB0vfaAhHQJdvRAAyVOd1fZQoaAZHQHF6ogV45cVoB00MAWgIR0CXb+rq+rU9dX2UKGgGR0Bteoaef7JoaAdL4WgIR0CXcTMC9ytFdX2UKGgGR0BwNhQSBbwCaAdL/mgIR0CXcWl1r6+GdX2UKGgGR0Bm9pyXD3ueaAdNFANoCEdAl3H6QiiZfHV9lChoBkdAcTz/0NBnjGgHTQgBaAhHQJdylYzSCvp1fZQoaAZHQHIEkAtFrmBoB00tAWgIR0CXc6lHjIaMdX2UKGgGR0BwquXIEKVqaAdL8GgIR0CXc7LsKLKndX2UKGgGR0BwMcBOpKjBaAdL02gIR0CXdKlJYkmhdX2UKGgGR0BpWuNgjQiSaAdN9AFoCEdAl3TuHnEET3V9lChoBkdAch705U96kmgHS/JoCEdAl3VAbdadMHV9lChoBkdAb34xLTQVsWgHS/NoCEdAl3VXHvMKTnV9lChoBkdAcaYtrKvFFWgHS+JoCEdAl3VdLg4wRHV9lChoBkdASzw6jnFHa2gHS9hoCEdAl3Xcyi22HHV9lChoBkdAcJ2uc+aBqmgHS+toCEdAl3Xswg1WKnV9lChoBkdAciYymQ8wH2gHS+NoCEdAl3aAeq7yx3V9lChoBkdAcCcTkyULUmgHS+JoCEdAl3e46jnFHnV9lChoBkdAcRaCUHIIW2gHTTUBaAhHQJd36qU/wAl1fZQoaAZHQHBZcIiTt9hoB0vfaAhHQJd4JpBX0Xh1fZQoaAZHQHH/LwvxpcpoB0v8aAhHQJd4OqPwNLF1fZQoaAZHQG6Csi8nNPhoB0vYaAhHQJd5eZXuE251fZQoaAZHQG/UkQf6oEVoB0vQaAhHQJd6HfR/mT11fZQoaAZHQHBbC88La25oB0vSaAhHQJd60T0xubZ1fZQoaAZHQHIVS3XqZ+hoB00JAWgIR0CXeuW7OE/TdX2UKGgGR0BtsPkiliz+aAdL42gIR0CXe0IZZSvUdX2UKGgGR0By/3rLQokSaAdL9GgIR0CXe1bPhQ3xdX2UKGgGR0BymH56+nIiaAdL72gIR0CXe34JNTLodX2UKGgGR0BhrjUTcqOMaAdN6ANoCEdAl3vGpda+vnV9lChoBkdAcQxPNFBppWgHS85oCEdAl3v3Ytg8bXV9lChoBkdAchK1CgK4QWgHS+9oCEdAl3wZKFqSHXV9lChoBkdAcO4UM5OrQ2gHTQQBaAhHQJd8ogfU4Jh1fZQoaAZHQHESzlLeyiVoB0vWaAhHQJd9RtIkJKJ1fZQoaAZHQG5sJrtVrARoB0vgaAhHQJd9rck+otN1fZQoaAZHQHFOJ7b+Lm9oB0vbaAhHQJd92Ts6aLJ1fZQoaAZHQHG4668QI2RoB0vzaAhHQJd+V43WFvh1fZQoaAZHQG6E5SWJJoVoB0vYaAhHQJd+350r9VF1fZQoaAZHQG2aeyiVSoBoB0vhaAhHQJd/pznzQNV1fZQoaAZHQHCA4CU5dW1oB0vcaAhHQJeAIA6uGK11fZQoaAZHQHFUVNHpbEBoB0vtaAhHQJeAncFhXsB1fZQoaAZHQHBFuLm6oVFoB0vkaAhHQJeAuu8scyZ1fZQoaAZHQHFUz/2kBS1oB0v7aAhHQJeBam78Nx51fZQoaAZHQHCDAosqaw5oB0vHaAhHQJeBeYrrgO11fZQoaAZHQHB2ewC8vmJoB0vjaAhHQJeBdaV2Rq51fZQoaAZHQG6zu+h4+r5oB0vvaAhHQJeBjTqjaf11fZQoaAZHQHDwBJVbRnhoB0v1aAhHQJeB/gWJrL11fZQoaAZHQHEPbypaRp1oB0vDaAhHQJeCawnpjc51fZQoaAZHQG5ijzI3irFoB0vgaAhHQJeCs7yQPqd1fZQoaAZHQHHEAG0NSZVoB0vzaAhHQJeD8uSOinJ1fZQoaAZHQG7QVxS5y2hoB0viaAhHQJeEErYoRZl1fZQoaAZHQFAiaQV9F4NoB0vRaAhHQJeFGNNrTH91fZQoaAZHQG9SBePaL4xoB0vZaAhHQJeGdr56+nJ1fZQoaAZHQG5HN6ol2NhoB0vLaAhHQJeHJ57gKnh1fZQoaAZHQHIBsifQKKJoB0vQaAhHQJeHNM+NcW11fZQoaAZHQHJV/wVj7Q9oB01YAWgIR0CXiDbYsd1ddX2UKGgGR0Bu8Z4bCJoCaAdL+GgIR0CXiF9d/rjYdX2UKGgGR0BwLvuVopQUaAdL92gIR0CXiGoF3Y+TdX2UKGgGR0ByvtmL9/BnaAdNLQFoCEdAl4h455qubXV9lChoBkdAcNCU5+6RQ2gHS9hoCEdAl4iqlchTwXV9lChoBkdAb7wquKXOW2gHS/FoCEdAl4jbX18LKHV9lChoBkdAcHyo/RmbsmgHTSsBaAhHQJeJClP8AJd1fZQoaAZHQHFe4qgAZKpoB0vsaAhHQJeJfHq/ub91fZQoaAZHQGSkm+j/MntoB01sA2gIR0CXieVfu1F6dX2UKGgGR0BxJL1EmY0EaAdL9mgIR0CXisyp71IzdX2UKGgGR0BylRMZgogFaAdNAAFoCEdAl4snb/Ot4nV9lChoBkdAbxcvxpcopmgHS+FoCEdAl4tY7Njbz3V9lChoBkdAcU5wblzU7WgHS9toCEdAl40WSpzcRHV9lChoBkdAbfe9FnZkCmgHS91oCEdAl400PMB6r3V9lChoBkdAcG8t03fhuWgHS9RoCEdAl430uxrzoXV9lChoBkdAccrKvmoze2gHTQ8BaAhHQJeODW1+iJx1fZQoaAZHQHGKv3rUsnRoB0vdaAhHQJeOXqX4TK11fZQoaAZHQHEA1yimEXdoB0vJaAhHQJeOgsoUi6h1fZQoaAZHQHCf7BoEjgRoB0vjaAhHQJeOnhisnzB1fZQoaAZHQG2lShi9ZidoB0vqaAhHQJeOv+0gKWt1fZQoaAZHQG6+Xw1BMSNoB0vaaAhHQJeOxNDc/MZ1fZQoaAZHQG+XhxPwd81oB00KAWgIR0CXj9pzcRDkdX2UKGgGR0BxM4DPnjhlaAdL8mgIR0CXkBAeJYT1dX2UKGgGR0BxNwQf6oETaAdL42gIR0CXkRTNt65YdX2UKGgGR0ByxVsrNGExaAdNCgFoCEdAl5EmqHXVb3V9lChoBkdAb5/fMwDeTGgHS9doCEdAl5EkIomXxHV9lChoBkdAcrrPUaya/mgHS95oCEdAl5GDF6zE8HVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}