ppo-LunarLander-v2 / config.json
migolan's picture
Upload PPO LunarLander-v2 trained agent
5460eb9 verified
{"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 0x79073d3f1bd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79073d3f1c60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79073d3f1cf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79073d3f1d80>", "_build": "<function ActorCriticPolicy._build at 0x79073d3f1e10>", "forward": "<function ActorCriticPolicy.forward at 0x79073d3f1ea0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79073d3f1f30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79073d3f1fc0>", "_predict": "<function ActorCriticPolicy._predict at 0x79073d3f2050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79073d3f20e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79073d3f2170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79073d3f2200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7906dfc9bfc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1735015030545775453, "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": -0.015808000000000044, "_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:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}