ppo-LunarLander-v2 / config.json
DBusAI's picture
Retrain PPO model for LunarLander-v2 v3
23d5286
{"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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f03879ae440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f03879ae4d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f03879ae560>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f03879ae5f0>", "_build": "<function ActorCriticPolicy._build at 0x7f03879ae680>", "forward": "<function ActorCriticPolicy.forward at 0x7f03879ae710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f03879ae7a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f03879ae830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f03879ae8c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f03879ae950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f03879ae9e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f03879f6990>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVbAAAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARSZUxVlJOUjAhuZXRfYXJjaJRdlH2UKIwCcGmUXZQoS4BLgEuAZYwCdmaUXZQoS4BLgEuAZXVhdS4=", "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>", "net_arch": [{"pi": [128, 128, 128], "vf": [128, 128, 128]}]}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1212416, "_total_timesteps": 1200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651697997.104268, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGZoq7yUrrw/xjCXvrRypD5lT7Y8VrsSPQAAAAAAAAAATXulPc9bND3/TR6+FeMUvinECz0O95W9AAAAAAAAAABAQSg+CtONP52F2T6a6Tu/n7xtPsppSj4AAAAAAAAAAM2thL2c+Tw+e5AjPoTIAr+j32q9P06CPgAAAAAAAAAAANC2uxBmwz8TNNy8RYKzvE9Fzzv6scQ7AAAAAAAAAABmqTO9J8+KPozSxD12fia/1tRVOwiiIT4AAAAAAAAAAIBChj3fQ5E86Sieviu1ab5pQ129WtH+vAAAAAAAAAAAjVGCPk8tOT/uzcE+B48ev3+c0j70fA8+AAAAAAAAAADNyEQ83R5tP51ggD3/lH2/pjXPvKu/H7sAAAAAAAAAAO3VFL6mypw/c4GfvlDPP7/qJ42+bWFQvgAAAAAAAAAAWq1WPjseAj8Oaha9MN3mvpdabT76Zuu9AAAAAAAAAAB6DgQ+ljuePpoctLw/pAi/6I3lPZvJab0AAAAAAAAAAGZJRT3D8Qq6Q65fM5mYWa9pP7+43lm0swAAgD8AAIA/JoW4vRRik7oYuAe+tvU9tvlaKjuiTak1AAAAAAAAgD/A3sQ9JFc2PNsTjL0AjEu+iJOdPbY7ljoAAAAAAAAAAM2fp7xUtgk/tyKXPJXJcL9dl4i9MryGPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.010346666666666726, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 370, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL29wdC9jb25kYS9saWIvcHl0aG9uMy43L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL29wdC9jb25kYS9saWIvcHl0aG9uMy43L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}