{"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 0x7874e5da40d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7874e5da4160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7874e5da41f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7874e5da4280>", "_build": "<function ActorCriticPolicy._build at 0x7874e5da4310>", "forward": "<function ActorCriticPolicy.forward at 0x7874e5da43a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7874e5da4430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7874e5da44c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7874e5da4550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7874e5da45e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7874e5da4670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7874e5da4700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7874e5d05b80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729774554343692850, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADMmdr1IdoY+2MwkPniYgb4sQDE6AlCOOwAAAAAAAAAATbkSPY8yRjkLTAK84yI5Ol0B9rm20v27AAAAAAAAAABmZ4i9WrCcP2iGJr61FW2+oIDnvZKiqr0AAAAAAAAAADPdJTxc+166E+nbOubcQTU+ttU6pLgAugAAgD8AAIA/s5cUPXvWlroIb9U43hTXMzz/1zoqpfa3AACAPwAAgD/zOpM9UvCkuWsPXTlkTE028J9Nu2FRg7gAAIA/AAAAAABqXzzs8aa7NGwQvL+goDx7rvG8E9GHPQAAgD8AAIA/jVvKPa75j7rFaV23Z62IsrdsqTqbR382AACAPwAAgD/Nnqq+EvYpP0IpZT40Zo2+XQC2vWItIj4AAAAAAAAAAJrBBD32EGi6rhpuu6HvuLZ8RCi7u/YlNgAAgD8AAIA/gDxFvfasDroZYiC4UdTtsqDtcjo2hjs3AACAPwAAgD+axMs8w518uoZsjTnX34A0TOoSuzq9pLgAAIA/AACAP83P1D0DEHI9zWPavPaWSb7T9r+6itpDvQAAAAAAAAAAM/YJvVzDM7pCzdU4RORmsHi1Prqblvm3AACAPwAAgD/N/Eq8j1IGur3krTpzVX41SA8FO7VU0LkAAIA/AACAP2ZGjjopDHq6vHrIt94cITX/ZwE7jf3oNgAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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 Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}