a2c-PandaReachDense-v2 / config.json
Dsfajardob's picture
Initial commit
4736b35
raw
history blame
15.5 kB
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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 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: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 MultiInputActorCriticPolicy.__init__ at 0x7fa859b81040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa8bb6c2d00>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682454242572109986, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.41966602 0.04919706 0.62076694]\n [0.41966602 0.04919706 0.62076694]\n [0.41966602 0.04919706 0.62076694]\n [0.41966602 0.04919706 0.62076694]]", "desired_goal": "[[ 1.234086 0.6754484 0.98028976]\n [ 0.32383966 -0.8066732 0.9317774 ]\n [-0.7418625 1.5552589 1.1361437 ]\n [-1.2778656 -1.4116333 -1.5857422 ]]", "observation": "[[0.41966602 0.04919706 0.62076694 0.00802328 0.00185297 0.00155527]\n [0.41966602 0.04919706 0.62076694 0.00802328 0.00185297 0.00155527]\n [0.41966602 0.04919706 0.62076694 0.00802328 0.00185297 0.00155527]\n [0.41966602 0.04919706 0.62076694 0.00802328 0.00185297 0.00155527]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.05474022 0.00093261 0.28661928]\n [ 0.04130758 0.0899794 0.19603708]\n [-0.08839936 0.11251502 0.23969276]\n [ 0.12612203 -0.13606273 0.13232538]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}