| {"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 0x7fe3c29a71c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe3c29a7250>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe3c29a72e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe3c29a7370>", "_build": "<function ActorCriticPolicy._build at 0x7fe3c29a7400>", "forward": "<function ActorCriticPolicy.forward at 0x7fe3c29a7490>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe3c29a7520>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe3c29a75b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe3c29a7640>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe3c29a76d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe3c29a7760>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe3c29a77f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe3c299abc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 229376, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1687201151111856353, "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": -0.1468799999999999, "_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": 70, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |