joniponi's picture
Upload PPO LunarLander-v2 trained agent
0115270
raw
history blame contribute delete
No virus
14.5 kB
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f14446ded40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f14446dedd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f14446dee60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f14446deef0>", "_build": "<function ActorCriticPolicy._build at 0x7f14446def80>", "forward": "<function ActorCriticPolicy.forward at 0x7f14446e4050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f14446e40e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f14446e4170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f14446e4200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f14446e4290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f14446e4320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f144472dab0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1654717510.562574, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+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": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}