ppo-LunarLander-v2 / config.json
jeffchan's picture
Initial training pass
8f2f71e
{"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 0x7f08025ff820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f08025ff8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f08025ff940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f08025ff9d0>", "_build": "<function ActorCriticPolicy._build at 0x7f08025ffa60>", "forward": "<function ActorCriticPolicy.forward at 0x7f08025ffaf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f08025ffb80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f08025ffc10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f08025ffca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f08025ffd30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f08025ffdc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0802678b10>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "low_repr": "-inf", "high_repr": "inf", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVjAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc3RhcnSUSwCMBl9zaGFwZZQpjAVkdHlwZZSMBW51bXB5lGgIk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==", "n": 4, "start": 0, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672676955343177118, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM0lirxcUwe6tiS3uV6HurP8ekg7K9vWOAAAgD8AAIA/ANW3vOyB6bl7+K86gQeNNZEFbzth+YM0AACAPwAAgD+a6JQ9KSBdutYIIrzwJi62j962OrXanzUAAIA/AACAP+biAD4kyQU/wHR1vgFalr6uoU69sQ8ivQAAAAAAAAAAM01/vfbkObo9NWS6e5RftoiZdrqe6oQ5AACAPwAAgD9mDt27jxJwuqlHxroTWug1FpcMO00f5zkAAIA/AACAP1o5vD0rCYg/BpG+PTTlz75Dt8095gHOvQAAAAAAAAAAzWU3vXtIgrrFA6K6qaSXtZh5Mrs05Lw5AACAPwAAgD9NOFU+AHkhPwLSJL4S/m++zJ84PVBxoL0AAAAAAAAAAABpnbxIAYy6yBpXO+TKRzb+0hg7tZd5ugAAgD8AAIA/mkacvLa5hz6hfxM8jSUlvqgRSTsOGB07AAAAAAAAAABmR7O9e2KkuudfrLm0sZ+2GPKQuUMEvjgAAIA/AACAPwAgJzwURoW6IrOkOreBpzXGHTE7veG/uQAAgD8AAIA/5pF4PSnAL7ogrXA7wo1HNpHDMDpq5Y26AACAPwAAgD9N62S9FFSkujZo8zra7/g3mpU+uob+RbgAAIA/AACAPwBasjzDiQa6Jz+tueZPE7Tu3d67L/rLOAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.25.2"}}