a2c-PandaReachDense-v3 / config.json
egarciamartin's picture
Initial commit
eba28fa
{"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 0x7a6892ed3d90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a6892ee1340>"}, "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": 1692607408213572166, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.5771308 0.41743386 0.30985606]\n [ 1.0788904 0.4398545 0.23518272]\n [ 1.1149639 0.87405354 1.2536666 ]\n [-0.49371687 -0.461388 0.30831566]]", "desired_goal": "[[-0.09262327 0.8979861 1.3039706 ]\n [ 0.9607814 0.9667845 -1.3899784 ]\n [ 1.2617207 0.5874344 1.6875734 ]\n [-1.4474288 -1.1864264 -0.08049238]]", "observation": "[[-0.5771308 0.41743386 0.30985606 -0.76457644 1.6509999 0.86922663]\n [ 1.0788904 0.4398545 0.23518272 1.5709683 1.638725 -1.1418335 ]\n [ 1.1149639 0.87405354 1.2536666 1.2005111 -0.01597973 1.4698019 ]\n [-0.49371687 -0.461388 0.30831566 -1.016518 -1.6466001 0.81084985]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.11898144 -0.1385757 0.2250593 ]\n [-0.13415954 -0.14967091 0.12818782]\n [ 0.04665751 -0.02325965 0.12563013]\n [ 0.04131853 -0.11237963 0.03399146]]", "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.0", "OpenAI Gym": "0.25.2"}}