| {"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 0x7d4b67cd67a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d4b67cd6830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d4b67cd68c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d4b67cd6950>", "_build": "<function ActorCriticPolicy._build at 0x7d4b67cd69e0>", "forward": "<function ActorCriticPolicy.forward at 0x7d4b67cd6a70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d4b67cd6b00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d4b67cd6b90>", "_predict": "<function ActorCriticPolicy._predict at 0x7d4b67cd6c20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d4b67cd6cb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d4b67cd6d40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d4b67cd6dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d4b6efcd300>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1277216, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712588592350001866, "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": 0.7446144, "_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": 12470, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": 64, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 10, "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:": "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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": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |