| {"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 0x7f997703fe20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f997703feb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f997703ff40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9977048040>", "_build": "<function ActorCriticPolicy._build at 0x7f99770480d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9977048160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f99770481f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9977048280>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9977048310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f99770483a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9977048430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f99770484c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9977045180>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1003520, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713442289639373105, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABXwz77Qvvw4d5wQv7/MrjrcDAq/cnPUu091Bb+e1/e7lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwKGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0035199999999999676, "_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": 980, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True]", "bounded_above": "[ True True]", "_shape": [2], "low": "[-1.2 -0.07]", "high": "[0.6 0.07]", "low_repr": "[-1.2 -0.07]", "high_repr": "[0.6 0.07]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAwAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "3", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 4, "n_steps": 1024, "gamma": 0.9999, "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-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Thu Oct 5 21:02:42 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": "0.26.2"}} |