ppo-LunarLander-v2 / config.json
mscs's picture
Small tweaks results in 200 +/- 40
c764b41 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 0x7ac0c8e50670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ac0c8e50700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ac0c8e50790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ac0c8e50820>", "_build": "<function ActorCriticPolicy._build at 0x7ac0c8e508b0>", "forward": "<function ActorCriticPolicy.forward at 0x7ac0c8e50940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ac0c8e509d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ac0c8e50a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7ac0c8e50af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ac0c8e50b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ac0c8e50c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ac0c8e50ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ac0c8dd6d80>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 1048576, "_total_timesteps": 1048576, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1724675635346695575, "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": 0.0, "_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": 102, "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": 2048, "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": 3, "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": "2.3.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}