| {"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 0x784f9bca60e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784f9bca6170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784f9bca6200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784f9bca6290>", "_build": "<function ActorCriticPolicy._build at 0x784f9bca6320>", "forward": "<function ActorCriticPolicy.forward at 0x784f9bca63b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x784f9bca6440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784f9bca64d0>", "_predict": "<function ActorCriticPolicy._predict at 0x784f9bca6560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784f9bca65f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784f9bca6680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x784f9bca6710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784f9bc54e40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718734702718274586, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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 Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |