| {"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 0x798b13d56290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x798b13d56320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x798b13d563b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x798b13d56440>", "_build": "<function ActorCriticPolicy._build at 0x798b13d564d0>", "forward": "<function ActorCriticPolicy.forward at 0x798b13d56560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x798b13d565f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x798b13d56680>", "_predict": "<function ActorCriticPolicy._predict at 0x798b13d56710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x798b13d567a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x798b13d56830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x798b13d568c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x798b1c690580>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1706082661581569756, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAIAEOr02kXA/9V9tvN6Xub44G0+9ke8FvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 1, "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:": "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"}, "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": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |