{"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 0x7f1f08807eb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1f08807f40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1f0880c040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1f0880c0d0>", "_build": "<function ActorCriticPolicy._build at 0x7f1f0880c160>", "forward": "<function ActorCriticPolicy.forward at 0x7f1f0880c1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1f0880c280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1f0880c310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1f0880c3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1f0880c430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1f0880c4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1f0880c550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f1f08808d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684600165461357504, "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:": "gAWV+AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQG8pVqWTouCMAWyUTR0BjAF0lEdAlcoYwyqMnHV9lChoBkdAcTPUeMhoumgHS91oCEdAlcpt7ngYQHV9lChoBkdAcj37g88s+WgHS/poCEdAlcta2v0ROHV9lChoBkdAc1XrTpgTiGgHS/VoCEdAlctWW2PT5XV9lChoBkdAcp1SElE7XGgHTQIBaAhHQJXMNlQMx491fZQoaAZHQHJcqJ/G2kVoB0veaAhHQJXNJMURFql1fZQoaAZHQHAz+Z9d/rloB0vuaAhHQJXNrL1VYIV1fZQoaAZHQHIuzSPU8V5oB00rAWgIR0CVzcowmE5AdX2UKGgGR0BwZ654GD+SaAdL5mgIR0CVzeZlFtsOdX2UKGgGR0BwP9vCMxXXaAdL4WgIR0CVze7/n4fwdX2UKGgGR0BvC9lXiiqRaAdL/2gIR0CVz0Hbh3qzdX2UKGgGR0ByRyus90RwaAdL42gIR0CVz4UPhAGCdX2UKGgGR0By3l1uBMBZaAdL3WgIR0CVz59US7GvdX2UKGgGR0ByNgxYaHbiaAdL5WgIR0CVz987IT4+dX2UKGgGR0ByQ5gy/KyOaAdNEAFoCEdAldAUzO5avHV9lChoBkdAcIrWUbDMvGgHS+1oCEdAldByPhhpg3V9lChoBkdAcw5fseGO/GgHTRIBaAhHQJXQt6cAiml1fZQoaAZHQHPAEZJkGzNoB0vcaAhHQJXQ6tNi6QN1fZQoaAZHQGMuQUYbbURoB03oA2gIR0CV0PejmCAddX2UKGgGR0BuR+F8G9pRaAdL62gIR0CV0TlSjxkNdX2UKGgGR0Bwupat9x6waAdLymgIR0CV0s5ZKWcCdX2UKGgGR0Bvg7uKGcnWaAdL72gIR0CV0wePq9oOdX2UKGgGR0ByDrxVhkRSaAdNHwFoCEdAldNSwW3z+XV9lChoBkdAcLMh37k4m2gHS/ZoCEdAldOrHMlkY3V9lChoBkdAcbpWTHKfWmgHS/JoCEdAldPP9pAUtnV9lChoBkdAcq8t0FKTS2gHTR4BaAhHQJXUyQaJhv11fZQoaAZHQHBpilN1yNpoB0veaAhHQJXVZVAAyVR1fZQoaAZHQHH8gjQiRnxoB0vRaAhHQJXVcyi22G91fZQoaAZHQHInhQzk6tFoB0vpaAhHQJXVeyB06o51fZQoaAZHQHE/PQjUuthoB0v6aAhHQJXVj7+DOC51fZQoaAZHQHMNIaLn9vVoB0v+aAhHQJXVvm0VrRB1fZQoaAZHQHItYwyqMm5oB0vRaAhHQJXVutxMnJF1fZQoaAZHQHKEeM2m52BoB00OAWgIR0CV1c4QjD8+dX2UKGgGR0Bx4xv73wkPaAdL0mgIR0CV1fAHmig1dX2UKGgGR0ByDSn5zo2XaAdL/WgIR0CWA7guyu6mdX2UKGgGR0Bwwg/Z/Tb4aAdNDgFoCEdAlgRq9kBjnXV9lChoBkdAb+R16E8JU2gHS9xoCEdAlgTSIpH7QHV9lChoBkdActKPOY6XB2gHS/ZoCEdAlgWsVpKzzHV9lChoBkdAb8tH+ZPVNGgHS+1oCEdAlgW/mozeoHV9lChoBkdAcVW9DQZ4wGgHS/hoCEdAlgZo20iQk3V9lChoBkdAbzIQ9zOopGgHTQMBaAhHQJYG3j1f3N91fZQoaAZHQHDDbzoUzsRoB0vWaAhHQJYHZ3EAHVx1fZQoaAZHQHLdX3lCCz1oB00BAWgIR0CWB8/G2kSFdX2UKGgGR0BwoTtG/etTaAdL6mgIR0CWCA1M/QjVdX2UKGgGR0Bviipo9LYgaAdL+GgIR0CWCD3NLUTddX2UKGgGR0BxsZyOq//OaAdL6GgIR0CWCHIGQjlgdX2UKGgGR0BwgBs/IKc/aAdL8mgIR0CWCHebd8ArdX2UKGgGR0BysEtHxz7uaAdL92gIR0CWCKGetjkNdX2UKGgGR0Bx6RKg7HQyaAdNFwFoCEdAlglFzZHuqnV9lChoBkdAbIl2A5JbuGgHS/5oCEdAlgoIjjaPCHV9lChoBkdAcnnSElE7XGgHTUgBaAhHQJYKMCeVcD91fZQoaAZHQHM6auGKyfNoB0v+aAhHQJYKrsRg7YF1fZQoaAZHQHBglDOTq0NoB0v9aAhHQJYLEcsDnvF1fZQoaAZHQHQ+Bl+Vkc1oB0vZaAhHQJYLGvUz9CN1fZQoaAZHQHFSH7DVH4JoB0veaAhHQJYLKFRHf/F1fZQoaAZHQHNK2cnVoYhoB0vjaAhHQJYL/os7MgV1fZQoaAZHQHDu22b5M11oB0vXaAhHQJYMHHeaa1F1fZQoaAZHQHEFSk0rK/5oB0vgaAhHQJYMy7SRbKR1fZQoaAZHQHKyJr+HaexoB0vUaAhHQJYM3FglWwN1fZQoaAZHQHAGYLCvX9RoB0vTaAhHQJYNZTqB3A51fZQoaAZHQHJoPKEFnqVoB0vhaAhHQJYNtOZb6gx1fZQoaAZHQHFbifg75mBoB00QAWgIR0CWDqEM9bHIdX2UKGgGR0BzHcmhM8HOaAdNIgFoCEdAlg7oeLehwnV9lChoBkdAco3OvMbFTGgHS/RoCEdAlg8Q6dUbUHV9lChoBkdAUzqpYLb5/WgHS6toCEdAlg8nlXA/LXV9lChoBkdAcKKg8bJfY2gHS+hoCEdAlg97gKnei3V9lChoBkdAcGFQ7cO9WmgHS+JoCEdAlhBh7/n4f3V9lChoBkdAcQCXyAhB7mgHS+doCEdAlhCV23azvHV9lChoBkdAcToVVghKUWgHTRQBaAhHQJYQp0Lc9GJ1fZQoaAZHQHFELHEMspZoB00PAWgIR0CWEQbpNbkfdX2UKGgGR0BxYpiONo8IaAdL9WgIR0CWEaraufVadX2UKGgGR0ByocXwb2lEaAdNCQFoCEdAlhI+XNTtLXV9lChoBkdAcb3lsP8Q7WgHS9ZoCEdAlhJimhufmXV9lChoBkdAcoNOgQHzH2gHTQABaAhHQJYSwPkJa7p1fZQoaAZHQHFclSCOFQFoB0v+aAhHQJYSxrqMWGh1fZQoaAZHQHD575AQg9xoB0voaAhHQJYTHDziCJ51fZQoaAZHQHCoTLwF1SxoB03jAWgIR0CWE+0bcXWOdX2UKGgGR0Bxq4G+sYEXaAdL5WgIR0CWFEGPPszEdX2UKGgGR0ByNIQQL/jsaAdL6mgIR0CWFIX5FgDzdX2UKGgGR0BxXksz2vjfaAdNAAFoCEdAlhSgHJLdvnV9lChoBkdAcQG1SflIVmgHS/BoCEdAlhS7YTTOPnV9lChoBkdAcwDJAt4A0mgHS9xoCEdAlhW02pAD73V9lChoBkdAcE9sqrilzmgHS+ZoCEdAlhW+SSvC/HV9lChoBkdAcm2Dc/MW42gHTQ8BaAhHQJYVydMCcPR1fZQoaAZHQHIkOm3vx6RoB0v9aAhHQJYWh3LV4HJ1fZQoaAZHQHD9gvlEJBxoB0v5aAhHQJYW2w7kn1F1fZQoaAZHQHFDUSVW0Z5oB0v4aAhHQJYXezlcQiB1fZQoaAZHQHKDU9ECvHNoB0vwaAhHQJYX5m7J4jd1fZQoaAZHQHOc/DYRNAVoB0vuaAhHQJYYAPJ7sv91fZQoaAZHQHCZZgXuVopoB0vfaAhHQJYYAxBVuJl1fZQoaAZHQHKIyMHbAUNoB0vhaAhHQJYYE7jkuHx1fZQoaAZHQHMRPlIVdopoB0veaAhHQJYYVZ5iVjZ1fZQoaAZHQHNaeXRgJC1oB0vXaAhHQJYZPZGrjo91fZQoaAZHQHPH6iO/+KloB0vpaAhHQJYZYERradt1fZQoaAZHQHLTfJ3gUDdoB0vmaAhHQJYaIIldC3R1fZQoaAZHQHEy/NeMQ3BoB0v2aAhHQJYaSUeMhox1fZQoaAZHQHLHgIppeu5oB0veaAhHQJYa/aIvalF1fZQoaAZHQG+C66reZXxoB0vaaAhHQJYa+0fHPu51fZQoaAZHQHGf+6y0KJFoB0vgaAhHQJYbE/Tspod1fZQoaAZHQHOveHFglWxoB00ZAWgIR0CWG0PeYUnHdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "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": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "3.10.11", "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"}}