| {"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 0x77fd98f2dd80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x77fd98f2de10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x77fd98f2dea0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x77fd98f2df30>", "_build": "<function ActorCriticPolicy._build at 0x77fd98f2dfc0>", "forward": "<function ActorCriticPolicy.forward at 0x77fd98f2e050>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x77fd98f2e0e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x77fd98f2e170>", "_predict": "<function ActorCriticPolicy._predict at 0x77fd98f2e200>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x77fd98f2e290>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x77fd98f2e320>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x77fd98f2e3b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x77fd98eda140>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1724304388771703688, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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:": "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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": "2.3.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |