| {"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 0x7e536837c5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e536837c670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e536837c700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e536837c790>", "_build": "<function ActorCriticPolicy._build at 0x7e536837c820>", "forward": "<function ActorCriticPolicy.forward at 0x7e536837c8b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e536837c940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e536837c9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7e536837ca60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e536837caf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e536837cb80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e536837cc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e536830e100>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712740024537754935, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAE0air0kuHE+hjH3PVtILL6efdo8vJgpPQAAAAAAAAAAmg7DvOi+iLwSKWS6x3MwPLjA9D0vCRK9AACAPwAAgD+ztiI9bGXqPJsBXT0UL0O+hgh2vCkMvjwAAAAAAAAAAJPQCj4prDM5a5cYODVuYzRHRvc7ItI2twAAgD8AAIA/AJBlO0g3pLo6FDE0MLlGMOd4sLpy55mzAACAPwAAgD/aMRw+VZkDPyXucr4Sj32+WFBlvaDYfLsAAAAAAAAAAHPLFb43KlE/iCivuuNXYL5CSoo8AvNuPAAAAAAAAAAAmqgEPXysRz02vLA9f2lEvi0fqD1w6U+9AAAAAAAAAADaBY49w8k7unEnkbVvwT6w+bI7uje9sjQAAIA/AACAP82f4zyuGZS6zt/puUtJrbhs+a06RqUOOQAAgD8AAIA/Zm0XvZPKaT9W3Im98JmDvuXmwbzJYQW9AAAAAAAAAAB6Vgm+Y2qwPn8qAz4Q2XW+8UNQPZcImT0AAAAAAAAAABOte76Kx/k+RflnPeuTdr5mYCe9OrRTvQAAAAAAAAAAGigoPXvaibpHb7i0mt11LqoZWTkYzyUzAACAPwAAgD+aedY9KogoP9iNJ751KIO+5SkGvVhJer0AAAAAAAAAAAAtE72nWgg/+41mPNl/O7666LG8fy0RvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |