{"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 0x7bbef8455bd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7bbef8455c60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7bbef8455cf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7bbef8455d80>", "_build": "<function ActorCriticPolicy._build at 0x7bbef8455e10>", "forward": "<function ActorCriticPolicy.forward at 0x7bbef8455ea0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7bbef8455f30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7bbef8455fc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7bbef8456050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7bbef84560e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7bbef8456170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7bbef8456200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bbef844a800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689718908340680580, "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:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 496, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "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"}}