| {"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 0x7f44badec160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f44badec1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f44badec280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f44badec310>", "_build": "<function ActorCriticPolicy._build at 0x7f44badec3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f44badec430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f44badec4c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f44badec550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f44badec5e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f44badec670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f44badec700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f44badec790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f44baf852c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1719177039000719102, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGZm7DtgTY4+2mK6vLUni775m8A9dpl8vQAAAAAAAAAAM1ysPI9GY7qwGNG7OQGMPKALdbjiRHQ9AACAPwAAgD8AFIq8XA0kPmVL0rzA4ou+oTGgvTn2O70AAAAAAAAAADPsBD0paAa6+LOFOLwTRzNGumE64uiatwAAgD8AAIA/GhSlPiN7Qj9qdjc9ubsBv3gjLD+lwNK9AAAAAAAAAACmUeC94XiZuttYqLeXgZSyZEd2OgOiwTYAAIA/AAAAAM3wFb50fAo/chx4PEyyvr4Dwr69ipnSPQAAAAAAAAAAmv3zvES7jD1y65m9wwc9vgb5rr32IKu8AAAAAAAAAACaXqK8H03+uUJ+SrmedT60kgiaOjsWczgAAIA/AACAP83jmLzcO0q80zWEvOLJej03p4c9OOSyOgAAgD8AAIA/ityBviEoGr0b++u8dSVbu4zhhj6dBi88AACAPwAAgD/NfOm6aH7iPn+FCb66fa2+pIO/vWgK6roAAAAAAAAAAAAYFzxbTa09U7WLvB1+F74HHPK9BtV9PQAAAAAAAAAAmkMgvCn4PLogixS5ieXEMgm1ljvo7i84AACAPwAAgD+aZfe8vuKsPfA6bzxFD1i+VN5WvC1izb0AAAAAAAAAAKYcsb364I0/X5ipvvdnJr/TTiO+El/jvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWVBgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHPqfIjnmq6MAWyUS+OMAXSUR0COkGnNxEORdX2UKGgGR0Bxg45GSZBtaAdL3mgIR0COkFyKekHldX2UKGgGR0Bw395WzWwvaAdL6GgIR0COkJp0wJw9dX2UKGgGR0BxZh3MY/FBaAdL/WgIR0COkP0yP+4tdX2UKGgGR0BtG7G7z06HaAdNEgFoCEdAjpJ/CZWq+HV9lChoBkdAbsc0ojOcD2gHS+FoCEdAjpXp+lTFVHV9lChoBkdAcDzDej2zwGgHTRsBaAhHQI6WLuF6Avt1fZQoaAZHQHKvmtlqagFoB0viaAhHQI6WsTviLl51fZQoaAZHQHJko2n889xoB0vlaAhHQI6Wvv6TGHZ1fZQoaAZHQHD9dfPX05FoB0vqaAhHQI6YbYChew91fZQoaAZHQHFFXGjsUqRoB01jAWgIR0COmlGHYYixdX2UKGgGR0BvkWNWEK3NaAdL9WgIR0COnbRzBAObdX2UKGgGR0ByD1k7OmiyaAdNSAFoCEdAjp8cBltj1HV9lChoBkdAcGZdt2s7uGgHTRoBaAhHQI6gh6KLsKN1fZQoaAZHQHFtew1R+BpoB0vvaAhHQI6g9Tgl4Tt1fZQoaAZHQHDQsiOearpoB00FAWgIR0COokMqjJuEdX2UKGgGR0Bx7mSDAaegaAdNHAFoCEdAjqJxqoIfKnV9lChoBkdAcg3wevIOpmgHTQQBaAhHQI6icWfseGR1fZQoaAZHQHMYcvVVghNoB0v4aAhHQI6jdcGC7K91fZQoaAZHQHDYWMKkVN5oB000AWgIR0COpalsxfv4dX2UKGgGR0BxfHKcNH6NaAdL+WgIR0COpjWXC0ngdX2UKGgGR0BvvCQxN7BwaAdL+WgIR0COpsWFev6kdX2UKGgGR0Bu5KyprDZUaAdNAgFoCEdAjqbev6j323V9lChoBkdAb02I5YHPeGgHS/9oCEdAjqcZFgDzRXV9lChoBkdAbcrCD28IzGgHS/VoCEdAjqj6Ww/xD3V9lChoBkdActClZX+2mmgHTSEBaAhHQI6p/ocJdB11fZQoaAZHQHJHieNDMNdoB00BAWgIR0COrFu0CzTndX2UKGgGR0BwHkngHeJpaAdL7mgIR0COrJcEeQuFdX2UKGgGR0Bx7mdEsrd4aAdL1mgIR0COrQBmwqy4dX2UKGgGR0BuK4oAn2IwaAdL5GgIR0COrWGIKtxNdX2UKGgGR0BxGog4ffXPaAdL3WgIR0COrqkdFOO9dX2UKGgGR0Byws5DJEH/aAdL9mgIR0COsBhkRSP2dX2UKGgGR0BxMfp1RtP6aAdNAAFoCEdAjrCAG0NSZXV9lChoBkdAcJUEFW4mTmgHS/JoCEdAjrDDr7fpEHV9lChoBkdAcU/SwGGEf2gHS/JoCEdAjrK63y7PIHV9lChoBkdAclmOd5IH1WgHS+JoCEdAjrMSgPEsKHV9lChoBkdAcCj/yoXKsGgHS+1oCEdAjrL/vv0AcXV9lChoBkdAb+MQnQY1pGgHS+5oCEdAjrOW8h9srXV9lChoBkdAbdcDZlFtsWgHS/xoCEdAjrSt6w+t83V9lChoBkdAcPcPGACnxmgHS/doCEdAjtjLc9GI9HV9lChoBkdAcjGBbOeJ52gHS9BoCEdAjttLronrp3V9lChoBkdAcyj9d/rjYWgHS+NoCEdAjtthmPHT7XV9lChoBkdAciQ1LJ0W/WgHTR0BaAhHQI7cU3Q2MsJ1fZQoaAZHQHHnuejEehhoB0vyaAhHQI7cgaJhvzh1fZQoaAZHQHGUHo5ggHNoB0vvaAhHQI7eh3aBZp11fZQoaAZHQHNqcj/uLJloB00QAWgIR0CO3rDAJswddX2UKGgGR0BSJeFcpsoEaAdLwmgIR0CO4GE9t/FzdX2UKGgGR0ByQZiuuA7QaAdL9mgIR0CO4QxptaZAdX2UKGgGR0BwDDLpzLfUaAdL+GgIR0CO4W5byH2zdX2UKGgGR0BxQ39JjDsMaAdL1mgIR0CO4e7JW/8EdX2UKGgGR0BxgyIk7fYSaAdNEAFoCEdAjuIofSx7iXV9lChoBkdAYM/nQID5kGgHTRwDaAhHQI7idapxWDJ1fZQoaAZHQHI+jPrv9cdoB0vkaAhHQI7ipEQXhwV1fZQoaAZHQG18/WMCLdhoB0vvaAhHQI7jlOIqLCN1fZQoaAZHQHOZ+mJm/WVoB0vlaAhHQI7lgqTbFjx1fZQoaAZHQHDnk4R28qZoB00ZAWgIR0CO5miNbTttdX2UKGgGR0BxiIaya/h3aAdL12gIR0CO6cDlHSWrdX2UKGgGR0By6KT8pCrtaAdNFAFoCEdAjun67/XGwXV9lChoBkdAcJpSVnmJWWgHS/9oCEdAjunrC3w1BXV9lChoBkdAb7RoNd7fHmgHTRUBaAhHQI7qGgte2NN1fZQoaAZHQHD2DbzshPloB00bAWgIR0CO6y3z+WGAdX2UKGgGR0Bwuur0aqCIaAdL+2gIR0CO62pLEk0KdX2UKGgGR0BxfHgvUSZjaAdL82gIR0CO7Hpvgm7bdX2UKGgGR0BwYGXNTtLMaAdL8GgIR0CO7XJaq0dBdX2UKGgGR0BxWy+K0lZ6aAdL8WgIR0CO7iYP5HmSdX2UKGgGR0ByDIOjIq9XaAdNCwFoCEdAju7yU1Q663V9lChoBkdAccBa8Yht+GgHTQoBaAhHQI7wIBq9Gqh1fZQoaAZHQHCgl4gRsdloB00FAWgIR0CO8GRxLkCFdX2UKGgGR0BwY9+LFXJYaAdNDQFoCEdAjvCzpHI6sHV9lChoBkdAb0fdVNpM6GgHS+loCEdAjvQEEC/47HV9lChoBkdAbuYd9Ujs2WgHTQ8BaAhHQI71byOJcgR1fZQoaAZHQHGhBArxy4poB01aAWgIR0CO94cawUxmdX2UKGgGR0Bz2L1OCXhPaAdL2WgIR0CO9/qubI91dX2UKGgGR0BwC64Ajps5aAdNAgFoCEdAjvrpN0vGqHV9lChoBkdAclNMSsbNr2gHTQQBaAhHQI77EIkZ75V1fZQoaAZHQG2Yg3DNyHVoB00RAWgIR0CO+9u+h4+sdX2UKGgGR0Bwmr1EmY0EaAdNAgFoCEdAjvzoV2zOX3V9lChoBkdAcZLhMrVe8mgHS+1oCEdAjv1V4gRsdnV9lChoBkdAcWJcT8HfM2gHS+toCEdAjv4vz4DcM3V9lChoBkdActUEAHVwxWgHS+ZoCEdAjv58RDkU9XV9lChoBkdAcYQCb+cYqGgHS+NoCEdAjv8ITGo73nV9lChoBkdAcu7UJfICEGgHTR8BaAhHQI7/VIRRMvh1fZQoaAZHQHO8t70Fr2xoB0v4aAhHQI8BjG5tm+V1fZQoaAZHQHHZXta6jFhoB00qAWgIR0CPBWALiMo+dX2UKGgGR0BvbvoNd7fIaAdL82gIR0CPBYiUxEfDdX2UKGgGR0BzGyzru6VdaAdNCQFoCEdAjwib7Kq4pnV9lChoBkdAcl8r/Khcq2gHS85oCEdAjwmHBLwnY3V9lChoBkdAcoJOJLuhK2gHTQQBaAhHQI8KHw7T2Fp1fZQoaAZHQG7Y55zHS4RoB00EAWgIR0CPCmXAuZkTdX2UKGgGR0BwheOYIBzWaAdL3GgIR0CPCmNkvsZ6dX2UKGgGR0BzGd4jbBXTaAdL7WgIR0CPC9Q2uPmxdX2UKGgGR0ByTfPX05EMaAdL+2gIR0CPDUhwEQoTdX2UKGgGR0BwbKYPXkHVaAdL3GgIR0CPDTdszl90dX2UKGgGR0Bwb44//vORaAdL/2gIR0CPDbtPYWcjdX2UKGgGR0ByKkoPTXrdaAdL9WgIR0CPDh2ugYgrdX2UKGgGR0Bxk5npSrHVaAdNCQFoCEdAjw7dMbm2cHV9lChoBkdAcGC0Rvm5lWgHS/toCEdAjw7yULUkOnV9lChoBkdAcgk5zo2XLWgHTQsBaAhHQI8RT9bX6Ip1fZQoaAZHQHLSuUhV2idoB0vqaAhHQI8SdzIV/MJ1fZQoaAZHQHBGdycTakBoB0vkaAhHQI8UbKifxtp1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 252, "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 Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |