| {"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 0x78aab3bbdf30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78aab3bbdfc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78aab3bbe050>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78aab3bbe0e0>", "_build": "<function ActorCriticPolicy._build at 0x78aab3bbe170>", "forward": "<function ActorCriticPolicy.forward at 0x78aab3bbe200>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78aab3bbe290>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78aab3bbe320>", "_predict": "<function ActorCriticPolicy._predict at 0x78aab3bbe3b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78aab3bbe440>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78aab3bbe4d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78aab3bbe560>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78aab3bb5d80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1742244336053875679, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAADadii+bGiTPGFyoT1aZ4O+hTQRPYJQZz0AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_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": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "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:": "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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-6.8.0-52-generic-x86_64-with-glibc2.39 # 53-Ubuntu SMP PREEMPT_DYNAMIC Sat Jan 11 00:06:25 UTC 2025", "Python": "3.10.16", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.2.4", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1"}} |