DrNykterstein's picture
Upload PPO LunarLander-v2 trained agent
dd528f7
raw
history blame contribute delete
No virus
14.6 kB
{
"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 0x00000215DA035160>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x00000215DA0351F0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x00000215DA035280>",
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x00000215DA035310>",
"_build": "<function ActorCriticPolicy._build at 0x00000215DA0353A0>",
"forward": "<function ActorCriticPolicy.forward at 0x00000215DA035430>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x00000215DA0354C0>",
"_predict": "<function ActorCriticPolicy._predict at 0x00000215DA035550>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x00000215DA0355E0>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x00000215DA035670>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x00000215DA035700>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x00000215D9DA6D00>"
},
"verbose": 1,
"policy_kwargs": {},
"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": 1376256,
"_total_timesteps": 1459872,
"_num_timesteps_at_start": 1359872,
"seed": null,
"action_noise": null,
"start_time": 1658377028.539719,
"learning_rate": 0.0003,
"tensorboard_log": null,
"lr_schedule": {
":type:": "<class 'function'>",
":serialized:": "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"
},
"_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": 0.057276254356546374,
"ep_info_buffer": {
":type:": "<class 'collections.deque'>",
":serialized:": "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"
},
"ep_success_buffer": {
":type:": "<class 'collections.deque'>",
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
},
"_n_updates": 332,
"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
}