a2c-PandaReachDense-v3 / config.json
sanitas's picture
Initial commit
939fc5c
{"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 0x7e24c3e456c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e24c3e3ec80>"}, "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": 1691543984668867710, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.8732623 0.81012124 -1.2928158 ]\n [ 0.26211202 0.00798574 0.42951483]\n [ 0.44550282 -0.40642786 -0.03067419]\n [-0.25598863 -0.43261078 0.46301377]]", "desired_goal": "[[-1.0303606 0.3891865 -0.5655759 ]\n [-1.504454 0.8522906 -0.09836649]\n [ 0.9233884 -0.586297 -0.7145217 ]\n [-0.40984234 -1.5562897 1.5223432 ]]", "observation": "[[-0.8732623 0.81012124 -1.2928158 -1.013553 -0.10688604 -0.9384327 ]\n [ 0.26211202 0.00798574 0.42951483 0.4566852 -0.00413107 0.38133824]\n [ 0.44550282 -0.40642786 -0.03067419 -0.05828407 -1.606999 -1.4788538 ]\n [-0.25598863 -0.43261078 0.46301377 0.05783663 -1.6563227 1.1028378 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.01522786 -0.02299193 0.04851112]\n [ 0.01294741 0.00338718 0.23465697]\n [-0.14059226 0.08698737 0.11182883]\n [ 0.08808073 0.00148342 0.27530682]]", "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv6LcsUZeiSKMAWyUSwGMAXSUR0CkfXkTpPhydX2UKGgGR7/V6+FlCkXUaAdLBWgIR0CkfUNMoMKDdX2UKGgGR7/FDfFaSs8xaAdLAmgIR0CkffoPTXrddX2UKGgGR7/S6NlyzXz2aAdLA2gIR0Ckfb/pljEvdX2UKGgGR7/K9JSR8twraAdLA2gIR0CkfYkLQXyidX2UKGgGR7+/dEb5uZTiaAdLAmgIR0CkfUz0xubadX2UKGgGR7+7UVi4J/oaaAdLAmgIR0CkfgOlfqoqdX2UKGgGR7+09yLhrFfiaAdLAmgIR0Ckfclq8DjjdX2UKGgGR7++wwCbMHKPaAdLAmgIR0CkfZH/95yEdX2UKGgGR7/DvOQhfShKaAdLAmgIR0CkfVXFLnLadX2UKGgGR7+oMfA9FF2FaAdLAWgIR0CkfZkdeY2LdX2UKGgGR7+NJJ5E+gUUaAdLAWgIR0CkfVzV+Zw5dX2UKGgGR7/Q3b212JSBaAdLA2gIR0CkfhNT987ZdX2UKGgGR7/Xtf5ULlV+aAdLBGgIR0Ckfd3EZR8/dX2UKGgGR7/LrJr+HaexaAdLA2gIR0CkfaYm1IAfdX2UKGgGR7/R8aXKKYReaAdLA2gIR0CkfWnPVurIdX2UKGgGR7/AEUTL4etCaAdLAmgIR0CkfeXkgfU4dX2UKGgGR7/Xq2BreqJeaAdLBGgIR0CkfiZ7w8W9dX2UKGgGR7/BULlV94NaaAdLAmgIR0CkfXQSBbwCdX2UKGgGR7+ikj5bhWHUaAdLAWgIR0CkfiqLS/j9dX2UKGgGR7+71f3N9ph4aAdLAmgIR0CkffAk9lmOdX2UKGgGR7/QCr92ovSMaAdLA2gIR0CkfbS+QEIPdX2UKGgGR7/CKgqVhTfjaAdLAmgIR0CkfXzYNAkcdX2UKGgGR7+40zj3mFJyaAdLAmgIR0CkfjOlGgBcdX2UKGgGR7/BroGIKtxNaAdLAmgIR0CkffmKZUkwdX2UKGgGR7+5Jrcj7hvSaAdLAmgIR0Ckfb6Eal1sdX2UKGgGR7+/QswtapxWaAdLAmgIR0CkfYb2criEdX2UKGgGR7/JOv+wTufFaAdLA2gIR0CkfgqsuFpPdX2UKGgGR7/G6xxDLKV6aAdLA2gIR0Ckfc+EytV8dX2UKGgGR7/MM85jpcHGaAdLA2gIR0CkfZdd/rjYdX2UKGgGR7/Yi35N47iiaAdLBWgIR0Ckfk3YUWVNdX2UKGgGR7/CmLLpzLfUaAdLAmgIR0CkfhOKO1fFdX2UKGgGR7/RWwNb1RLsaAdLA2gIR0CkfdwlKK51dX2UKGgGR7/T0fYBeXzEaAdLA2gIR0CkflxlxwQ2dX2UKGgGR7/Wr7fpD/lyaAdLBGgIR0CkfaoTfzjFdX2UKGgGR7/bFnqVyFPBaAdLBGgIR0CkfiZEUj9odX2UKGgGR7/AYYzi0fHQaAdLA2gIR0CkferqMWGidX2UKGgGR7/C1x82Jiy6aAdLAmgIR0Ckfi6GpMpPdX2UKGgGR7/Fm/336AOKaAdLA2gIR0CkfbbMHKOldX2UKGgGR7/UydFvybx3aAdLBGgIR0Ckfm+9zwMIdX2UKGgGR7/SLaEi+tbLaAdLA2gIR0Ckffnj6vaDdX2UKGgGR7+9udf9gnc+aAdLAmgIR0CkfcHFxXGPdX2UKGgGR7+92pyZKFqSaAdLAmgIR0CkfnhXr+o+dX2UKGgGR7/QEEC/47A+aAdLA2gIR0Ckfj4fOlfrdX2UKGgGR7+pFkQPI4lyaAdLAWgIR0CkfnzV2A5JdX2UKGgGR7/U7hNucc2jaAdLA2gIR0CkfgcJD3M7dX2UKGgGR7/DvuPV/c33aAdLAmgIR0CkfcrJbMX8dX2UKGgGR7+jqptJnQIEaAdLAWgIR0Ckfgtix3V1dX2UKGgGR7/I3WFvhqCZaAdLA2gIR0Ckfk2mgrYodX2UKGgGR7/OVGkN4JNTaAdLA2gIR0CkfoxASnLrdX2UKGgGR7+fEXLvCuU2aAdLAWgIR0CkflH62v0RdX2UKGgGR7/OH5aePJaJaAdLA2gIR0CkfdpO32EkdX2UKGgGR7/RIN3GGVRlaAdLA2gIR0CkfhrXDm8vdX2UKGgGR7/EbrkbPyCnaAdLAmgIR0CkflqBmPHUdX2UKGgGR7+Hos7MgU1yaAdLAWgIR0Ckfh8TJyQxdX2UKGgGR7/YHww0waisaAdLBGgIR0Ckfp71ZkkKdX2UKGgGR7/EnhsImgJ1aAdLAmgIR0CkfikUTL4fdX2UKGgGR7/W4zrNW2gGaAdLBGgIR0CkfezAWSEEdX2UKGgGR7/Ia8Yht+CsaAdLA2gIR0CkfmixNZeSdX2UKGgGR7+5wFTvRZ2ZaAdLAmgIR0CkfjDVx0dSdX2UKGgGR7/SmT1TR6WxaAdLA2gIR0CkfqrzXjEOdX2UKGgGR7/KQRwqAjIJaAdLA2gIR0CkffhysCDFdX2UKGgGR7/OsyzollbvaAdLA2gIR0CkfnRWcSXddX2UKGgGR7/ETq0MPSUkaAdLAmgIR0CkfrTTOPeYdX2UKGgGR7/QUnG8274BaAdLA2gIR0Ckfj7/GVAzdX2UKGgGR7+3opx3mmtRaAdLAmgIR0Ckfn5fUnXvdX2UKGgGR7/BqXWvr4WUaAdLAmgIR0CkfkaScLBsdX2UKGgGR7/hgeq7yxzJaAdLBGgIR0CkfgpA+pwTdX2UKGgGR7/PIV/MGHHnaAdLA2gIR0CkfsDJuEVWdX2UKGgGR7/DGMn7YTTOaAdLAmgIR0CkfoZksjFAdX2UKGgGR7/TVTrE9+w1aAdLA2gIR0Ckfs43m3fAdX2UKGgGR7/JBqKxcE/0aAdLA2gIR0CkfpQDV6NVdX2UKGgGR7/RoK2KEWZaaAdLBGgIR0CkfljCHh0hdX2UKGgGR7/WkwN9YwIuaAdLBGgIR0CkfhykTHsDdX2UKGgGR7+z5SFXaJyiaAdLAmgIR0Ckfp1fVqetdX2UKGgGR7+6rHU+cH4XaAdLAmgIR0CkfiVfmcOLdX2UKGgGR7/Tbg0j1PFeaAdLA2gIR0CkftvRqoIfdX2UKGgGR7/K65Gz8gp0aAdLA2gIR0CkfmXMyJsPdX2UKGgGR7+YGY8dPtUoaAdLAWgIR0CkfuHbZezEdX2UKGgGR7+71jAi3XqaaAdLAmgIR0Ckfqd8JD3NdX2UKGgGR7+zf642CNCJaAdLAmgIR0Ckfi+F+NLldX2UKGgGR7+620AtFrmAaAdLAmgIR0CkfrBbfP5YdX2UKGgGR7/JdweeWfK7aAdLA2gIR0CkfnTrNW2gdX2UKGgGR7/ZuhK15Sm7aAdLBGgIR0CkfvKQRwqBdX2UKGgGR7/a3QUpNKywaAdLBGgIR0CkfkAsbvPUdX2UKGgGR7/QEZR8+iaiaAdLA2gIR0CkfoKu0TlDdX2UKGgGR7++TeO4oZydaAdLAmgIR0CkfvyYPXkHdX2UKGgGR7/X0nPVurIYaAdLBWgIR0CkfsXBP9DQdX2UKGgGR7+61IAfdRBNaAdLAmgIR0Ckfopd0JWvdX2UKGgGR7/LHXEqDsdDaAdLA2gIR0Ckfk4nv2GqdX2UKGgGR7/SriEQGwA3aAdLA2gIR0Ckfwjlgc94dX2UKGgGR7+6Q8wHqu8saAdLAmgIR0CkfpL2HtWudX2UKGgGR7/C47Rv3rUtaAdLAmgIR0CkflavicXndX2UKGgGR7/LZdv863iJaAdLA2gIR0CkftTYEnstdX2UKGgGR7+hYJVsDW9UaAdLAWgIR0CkfplhXr+pdX2UKGgGR7/BjawljVhDaAdLAmgIR0CkfmB5X2dvdX2UKGgGR7/SUz9CNS62aAdLA2gIR0CkfxbwKBuodX2UKGgGR7/NAYYR/ViGaAdLA2gIR0CkfqRhc7hfdX2UKGgGR7+08HObAk9maAdLAmgIR0CkfmgBtDUmdX2UKGgGR7/XdLg4wRGuaAdLBGgIR0CkfuPmozeodWUu"}, "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": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}