{"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 0x7a5842e5dc60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a5842e5dcf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a5842e5dd80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a5842e5de10>", "_build": "<function ActorCriticPolicy._build at 0x7a5842e5dea0>", "forward": "<function ActorCriticPolicy.forward at 0x7a5842e5df30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a5842e5dfc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a5842e5e050>", "_predict": "<function ActorCriticPolicy._predict at 0x7a5842e5e0e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a5842e5e170>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a5842e5e200>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a5842e5e290>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a5842e4fac0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690707683917951893, "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": 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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.97, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "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"}}