| {"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 0x795bbeda4e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x795bbeda4ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x795bbeda4f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x795bbeda5000>", "_build": "<function ActorCriticPolicy._build at 0x795bbeda5090>", "forward": "<function ActorCriticPolicy.forward at 0x795bbeda5120>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x795bbeda51b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x795bbeda5240>", "_predict": "<function ActorCriticPolicy._predict at 0x795bbeda52d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x795bbeda5360>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x795bbeda53f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x795bbeda5480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x795b63d27900>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1728838051088236463, "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": 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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |