{"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 0x7e9cdc67ab90>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e9cdc67ac20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e9cdc67acb0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e9cdc67ad40>", "_build": "<function ActorCriticPolicy._build at 0x7e9cdc67add0>", "forward": "<function ActorCriticPolicy.forward at 0x7e9cdc67ae60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e9cdc67aef0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e9cdc67af80>", "_predict": "<function ActorCriticPolicy._predict at 0x7e9cdc67b010>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e9cdc67b0a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e9cdc67b130>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e9cdc67b1c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e9cdc615100>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1707125512127697251, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAACY4Nr5VFIY/5TbEvrqiGr+dOYG+EkwZvgAAAAAAAAAA2tT5PWlrJD6QFea9g9PCvsTjCD2oSla9AAAAAAAAAAAN5cQ9pK5Nu37qsbwrYg47kKK2PJu4G7wAAIA/AACAP2YpOD1eLxY/ebEMPYTPVL94PVo902drvQAAAAAAAAAAU2MRPimPIbztiho70ZkauWElhr0yAXa6AACAPwAAgD9zuha+9uZJvFW8/LlASjC4WBKuPTKVMjkAAIA/AACAP2bvND09eYo/jhEqPr6URr9lqLE9YRYtPAAAAAAAAAAAzYkOvdeNErsmzna8SwQCPerxGjxrltu9AACAPwAAgD9mjSg9iOyTPw3JYz6xJVa/rA2xPQGXST0AAAAAAAAAAADFz71czGg7zC2dPpLxcL72CuU9FYpmvgAAAAAAAIA/EyYmPn/brT5frxC+Hs8mv20OrDxAKGu9AAAAAAAAAABaULo9djl3vPJ17ryODLk8ccrbPTnyk70AAIA/AAAAALNRDL7NUdM+JICvvVyFH7+hNFO+xoNOvQAAAAAAAAAAsx4APfbUK7pZoCgzj6dVpnjuXDvRZcqzAACAPwAAgD+zUyi+h+PKPup/HL3rahi/9vo0vsBVubwAAAAAAAAAAJodHb7zFyM/Y6gVvvxLPr8vJ0W+wnULPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWV4gsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHHdpXQtz0aMAWyUS7OMAXSUR0Cpbk/CIk7fdX2UKGgGR0Bxo7mV7hNuaAdL8mgIR0CpbqFC9h7WdX2UKGgGR0BwaX1L8JlbaAdLqmgIR0Cpbqn5JsfrdX2UKGgGR0Bwod/rjYI0aAdLkWgIR0Cpbrz+NtIkdX2UKGgGR0Bx7YNVinYQaAdL0WgIR0CpbsJW/8EWdX2UKGgGR0BujjGrCFbnaAdLmWgIR0CpbwXueBhAdX2UKGgGR0BzYieRPoFFaAdLx2gIR0CpbxZjH4oJdX2UKGgGR0By6b3wkPc0aAdL1mgIR0Cpb0faQFLWdX2UKGgGR0Bz4Ci0v4/NaAdL2mgIR0Cpb1Cdz4lAdX2UKGgGR0ByMt6jWTX8aAdL1GgIR0Cpb3FuWKMvdX2UKGgGR0BxmwA4n4O+aAdLhmgIR0Cpb3u0svqUdX2UKGgGR0BzkWcZtNzsaAdLu2gIR0Cpb4SR0U48dX2UKGgGR0BxZfEzfrKOaAdLjmgIR0Cpb+GWD6FedX2UKGgGR0BmjKxoqTbGaAdN6ANoCEdAqW/kxbjcVXV9lChoBkdAcLxEytV7yGgHS7BoCEdAqXA9cGC7LHV9lChoBkdAc3BEaESM+GgHS71oCEdAqXA9RUFSsXV9lChoBkdAca7UYKpkw2gHS55oCEdAqXBXFcY64nV9lChoBkdAbpTl7MPjGWgHS6poCEdAqXB9FQVKw3V9lChoBkdAcyAlCkXUIGgHS9VoCEdAqXCFWQwK0HV9lChoBkdAcOm0WuX/pGgHS4toCEdAqXCJaLXL/3V9lChoBkdAcpmBikO7QWgHS7hoCEdAqXC3tBv733V9lChoBkdAcSLrPMSsbWgHS75oCEdAqXDCmIj4YnV9lChoBkdAb6oltTDO1WgHS5NoCEdAqXDTKvFFUnV9lChoBkdAcu9ivxH5J2gHS7loCEdAqXD3FPznR3V9lChoBkdAcp+rWRRuTGgHS6FoCEdAqXElk1/DtXV9lChoBkdAc/Cvq1PWQWgHS7RoCEdAqXEpVGTcI3V9lChoBkdAc3sixmkFfWgHS8ZoCEdAqXGm/WUbDXV9lChoBkdAbtFjz7MxGmgHS6JoCEdAqXHASBbwB3V9lChoBkdAcWi6AvtdA2gHS8xoCEdAqXHYZTAFgXV9lChoBkdAc62NKRMewWgHS71oCEdAqXIh7XxvvXV9lChoBkdAcz2jjJdSl2gHS6poCEdAqXJ/C4z7/HV9lChoBkdAcSkJ3gUDdWgHS4xoCEdAqXKreXRgJHV9lChoBkdAcS6H58BuGmgHS8FoCEdAqXKwgHNX5nV9lChoBkdAcf2r+o99t2gHS6loCEdAqXK+sPrfL3V9lChoBkdAcK4zl90A92gHS7VoCEdAqXLwfGMn7nV9lChoBkdAcazKPGQ0XWgHS41oCEdAqXL0OZssQXV9lChoBkdAcGURSgoPTWgHS79oCEdAqXMHA/LTyHV9lChoBkdAccM7ZFocrGgHS+FoCEdAqXMp46fapXV9lChoBkdAcY2jGT9sJ2gHS9VoCEdAqXPldTo+wHV9lChoBkdAcsukZaV2R2gHS5xoCEdAqXQXNeMQ3HV9lChoBkdAc2N1hb4agmgHS7hoCEdAqXR3Jgb6xnV9lChoBkdAcbEEsrd30WgHS5ZoCEdAqXR7CemNznV9lChoBkdAchfSMcZLqWgHS9loCEdAqXR7J0W/J3V9lChoBkdAcoeQtSQ5m2gHS6FoCEdAqXUaXrt3OnV9lChoBkdAc0Gwj+rEL2gHS9hoCEdAqXU+L3sXznV9lChoBkdAcVJDIzWPLmgHS5xoCEdAqXVHUhFEzHV9lChoBkdAb/KA9V3ljmgHS6doCEdAqXValxffGnV9lChoBkdAcBePS2H+ImgHS6JoCEdAqXWOlEZzgnV9lChoBkdAcZ1Dw6QvH2gHS6toCEdAqXWaG+K0lnV9lChoBkdAcX6hTOxB3WgHS6poCEdAqXWaYb83uXV9lChoBkdAdBi+K0lZ5mgHS8toCEdAqXW/XGwRoXV9lChoBkdAcyHAfuCwr2gHS8hoCEdAqXYZYmsvI3V9lChoBkdAcMFPbO/tY2gHS55oCEdAqXZL9MsYmHV9lChoBkdAc902gWac7WgHS61oCEdAqXa/JvHcUXV9lChoBkdAcwwHh0hePmgHS7ZoCEdAqXbbhegL7XV9lChoBkdAbu6QHzH0b2gHS5RoCEdAqXbkvh60IHV9lChoBkdAcT+pyIYWL2gHS91oCEdAqXbuJxeb/nV9lChoBkdAczQS9M9KVmgHS8ZoCEdAqXcPNTtLMHV9lChoBkdAc7mZLIxQBWgHS6toCEdAqXdTvy9VWHV9lChoBkdAcNzemvW6LGgHS6loCEdAqXdi7PIGQnV9lChoBkdAc/zZFG5MDmgHS7toCEdAqXd+aH9FWnV9lChoBkdAczyvaDf3vmgHS5poCEdAqXd8PMB6r3V9lChoBkdAcGBSgoPTX2gHS7JoCEdAqXe6uyNXHXV9lChoBkdAc6KDYywfQ2gHS79oCEdAqXfutp22X3V9lChoBkdAcR9klNUOu2gHS5doCEdAqXf3hbW3B3V9lChoBkdAcHSkyk9EC2gHS7JoCEdAqXiGGO+7DnV9lChoBkdAS1tfeDWbw2gHS4doCEdAqXiZXfZVXHV9lChoBkdAcXKu0kWykmgHS35oCEdAqXkIcvM8o3V9lChoBkdAc4VXf642CWgHS7toCEdAqXlSKm8/U3V9lChoBkdAdCZ0IC2c8WgHS8loCEdAqXll36hxpHV9lChoBkdAcIeFRYRuj2gHS6loCEdAqXmXBnBciXV9lChoBkdAc2s+DvmYB2gHS6xoCEdAqXnNHvttynV9lChoBkdAcnw7TUiIL2gHS65oCEdAqXnWscQyynV9lChoBkdAcaikpZwGW2gHS9RoCEdAqXnmvpyIYXV9lChoBkdAci/cfeUILWgHS/loCEdAqXo/OdGy5nV9lChoBkdAcoVxk/bCamgHS79oCEdAqXpUpb2US3V9lChoBkdAcXrT4L1EmmgHS7JoCEdAqXph0GNaQnV9lChoBkdAcWk6reZXuGgHS4poCEdAqXqIR7JGOXV9lChoBkdAc70Ujs2NvWgHS7poCEdAqXqHT/hl2HV9lChoBkdAb+uZWq94/2gHS6loCEdAqXrb2Bas63V9lChoBkdAcmaUL2HtW2gHS41oCEdAqXr0FlkH2XV9lChoBkdAcfyV09yLh2gHS6hoCEdAqXumlImPYHV9lChoBkdAcCiAxzq8lGgHS7FoCEdAqXuykhzNlnV9lChoBkdAcKPiB5HEuWgHS6doCEdAqXvUHt4RmXV9lChoBkdAcZHzGPxQSGgHS61oCEdAqXwbj3mFJ3V9lChoBkdAchZvAGjbjGgHS5poCEdAqXxucriEQHV9lChoBkdAcxPkk8ifQWgHS8loCEdAqXyHTw2ETXV9lChoBkdAc1t4dp7CzmgHS6doCEdAqXyRhnanJnV9lChoBkdAchcZbILgGmgHS7xoCEdAqXzHwAlv63V9lChoBkdAcFkN5MURF2gHS7NoCEdAqXzyF9KEnXV9lChoBkdAc6xtsvZh8mgHS/FoCEdAqX0kIC2c8XV9lChoBkdAcwAtp22Xs2gHS7hoCEdAqX1e9lEqlXV9lChoBkdAcLMR15jYqWgHS7RoCEdAqX1tNahYeXV9lChoBkdAcmnjC53C9GgHS9loCEdAqX14+EAYHnV9lChoBkdAbiUOS4e9z2gHS6ZoCEdAqX3+so2GZnV9lChoBkdAcVewsGxD9mgHS4loCEdAqX4ETxoZh3V9lChoBkdAcBg58jRlYmgHS61oCEdAqX4MLBsQ/XV9lChoBkdActoujh1klWgHS7NoCEdAqX5LeCTUzHV9lChoBkdAYtL/2Cdz4mgHTegDaAhHQKl+aQpWmxd1fZQoaAZHQHGBe/5+H8FoB0uOaAhHQKl+hL7oB7x1ZS4="}, "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": 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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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"}}