a2c-AntBulletEnv-v0 / config.json
shuojiang's picture
Initial commit
0099d72
raw
history blame
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 0x7ff3ad8f77a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff3ad8f7830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff3ad8f78c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff3ad8f7950>", "_build": "<function ActorCriticPolicy._build at 0x7ff3ad8f79e0>", "forward": "<function ActorCriticPolicy.forward at 0x7ff3ad8f7a70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff3ad8f7b00>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff3ad8f7b90>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff3ad8f7c20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff3ad8f7cb0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff3ad8f7d40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff3ad9439c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gASViwIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBVudW1weS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5RoBowHbmRhcnJheZSTlEsAhZRDAWKUh5RSlChLAUschZRoColDcAAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP+UdJRijARoaWdolGgSaBRLAIWUaBaHlFKUKEsBSxyFlGgKiUNwAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5R0lGKMDWJvdW5kZWRfYmVsb3eUaBJoFEsAhZRoFoeUUpQoSwFLHIWUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGKJQxwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlHSUYowNYm91bmRlZF9hYm92ZZRoEmgUSwCFlGgWh5RSlChLAUschZRoKolDHAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRijApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1665675830190635957, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.14", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}