a2c-PandaReachDense-v3 / config.json
hishamcse's picture
PandaReach Solved
c02dc09 verified
raw
history blame
No virus
14.3 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 0x7b12130f5f30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b1212fc0a40>"}, "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": 500000, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718978207166047434, "learning_rate": 0.0008, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-2.1145265 1.2109166 -1.0853802 ]\n [ 0.72918284 -0.33714497 0.24060969]\n [ 0.06962644 0.24335112 -0.08195139]\n [-0.20999551 -0.3750963 0.29461485]]", "desired_goal": "[[-1.5420191 1.5964426 -0.8900565 ]\n [ 1.3900167 -0.52887833 -0.58821005]\n [-1.1391845 1.3664321 -0.917825 ]\n [-0.0526975 -1.2307358 0.86009514]]", "observation": "[[-2.1145265 1.2109166 -1.0853802 -2.3020284 -0.47229606 -0.04864804]\n [ 0.72918284 -0.33714497 0.24060969 1.3710556 -1.6311586 -1.1927776 ]\n [ 0.06962644 0.24335112 -0.08195139 -1.7138622 1.7018869 -1.4610724 ]\n [-0.20999551 -0.3750963 0.29461485 -0.7836527 -1.7094939 0.8805507 ]]"}, "_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": "[[-0.14721797 0.14145952 0.18789679]\n [ 0.12971438 -0.05878402 0.2568012 ]\n [-0.1233416 0.11110318 0.28116846]\n [-0.0853532 0.07504939 0.21816327]]", "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:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv6eGwiaAnUmMAWyUSwGMAXSUR0CuzctfG+9KdX2UKGgGR7/Ifzz3AVO9aAdLA2gIR0CuzpAB91EFdX2UKGgGR7/NvVEuxrzoaAdLA2gIR0CuzW/GVAzIdX2UKGgGR7+eR9w3o9s8aAdLAWgIR0CuzpcrI5o5dX2UKGgGR7+7/KhcqvvCaAdLAmgIR0Cuzdd2HLzPdX2UKGgGR7+5sguAZsKtaAdLAmgIR0CuzXt4iX6ZdX2UKGgGR7/LronrpqyoaAdLA2gIR0CuzqWa2F37dX2UKGgGR7/OVwgkka/AaAdLA2gIR0CuzeZGz8gqdX2UKGgGR7/hZWaMJhOQaAdLB2gIR0Cuzkk7nxJ/dX2UKGgGR7/KN6PbO/tZaAdLA2gIR0CuzYuzY287dX2UKGgGR7/TQyhzvJA/aAdLA2gIR0Cuzrek56t1dX2UKGgGR7/IYiPhhpg1aAdLAmgIR0CuzlVl5GBndX2UKGgGR7++/Yao/A0saAdLAmgIR0CuzZeEytV8dX2UKGgGR7/Yoh6jWTX8aAdLBGgIR0Cuzfx2B8QadX2UKGgGR7/ElSCOFQEZaAdLAmgIR0CuzaA8KXv6dX2UKGgGR7/TxPfsNUfgaAdLA2gIR0CuzsWNvOyFdX2UKGgGR7/ShuO0b961aAdLA2gIR0CuzmNqpLmIdX2UKGgGR7/TzqbBoEjgaAdLA2gIR0Cuzgz+NtIkdX2UKGgGR7+8wDeTFERbaAdLAmgIR0CuztGs/6frdX2UKGgGR7/PiXIEKVpsaAdLA2gIR0CuzbFcY64ldX2UKGgGR7/J51vES/TLaAdLA2gIR0CuznQ0XP7fdX2UKGgGR7/CfRu0kWykaAdLAmgIR0CuzhbQ9ic5dX2UKGgGR7/B4k/r0J4TaAdLAmgIR0Cuztua4MF2dX2UKGgGR7/Q+jua4MF2aAdLA2gIR0CuzcAi3XqadX2UKGgGR7/IgvDgqEvkaAdLA2gIR0CuzieeWfK7dX2UKGgGR7/Q/oq0+kgwaAdLA2gIR0CuzuyQgcLjdX2UKGgGR7/bP4EfT1CgaAdLBGgIR0CuzopnHvMKdX2UKGgGR7+9gTh5xBE8aAdLAmgIR0CuzcznaFmGdX2UKGgGR7/N86V+qioLaAdLA2gIR0CuzjavRqoIdX2UKGgGR7/RorFwT/Q0aAdLA2gIR0CuzvtfPX05dX2UKGgGR7/SsbedkJ8faAdLA2gIR0CuzplbVz6rdX2UKGgGR7/AV2zOX3QEaAdLAmgIR0CuzkK+zt1IdX2UKGgGR7/YYuCf6Gg0aAdLBGgIR0CuzeKCpWFOdX2UKGgGR7/DV0cOskpraAdLAmgIR0CuzqURvm5ldX2UKGgGR7/XvWpZOi35aAdLBGgIR0CuzxB5xBE8dX2UKGgGR7+9j0+TvAoHaAdLAmgIR0Cuzq47Rv3rdX2UKGgGR7/LnfVI7NjcaAdLA2gIR0CuzfCFTNt7dX2UKGgGR7/e3h4t6HCXaAdLBGgIR0CuzlYAbQ1KdX2UKGgGR7+9Gsmv4dp7aAdLAmgIR0Cuzrpvo/zKdX2UKGgGR7/VF9a2WpqAaAdLA2gIR0CuzgGXPZ7HdX2UKGgGR7/hUd7v5P/JaAdLBGgIR0CuzybI1cdHdX2UKGgGR7/UuG9Htnf3aAdLA2gIR0CuzmbiIciodX2UKGgGR7/QumrKeTV2aAdLA2gIR0CuzskidJ8OdX2UKGgGR7+8LfDUExIraAdLAmgIR0CuzgtgjQiSdX2UKGgGR7/AYYzi0fHQaAdLAmgIR0CuzzBwVCXydX2UKGgGR7/HpRGc4HX3aAdLA2gIR0CuznbB42S/dX2UKGgGR7/GHMUypJf6aAdLA2gIR0CuztjMmnfmdX2UKGgGR7+UbHZK3/gjaAdLAWgIR0CuzntwJgLJdX2UKGgGR7/QZIQOFxn4aAdLA2gIR0CuzhsrmQr+dX2UKGgGR7/GVdonKGL2aAdLA2gIR0Cuz0A9/z8QdX2UKGgGR7+dNvfj0cwQaAdLAWgIR0Cuzt30PH1fdX2UKGgGR7+SULUkOZssaAdLAWgIR0Cuzh/vOQhfdX2UKGgGR7+jCN0eU6geaAdLAWgIR0Cuz0Ui6g/UdX2UKGgGR7/WRbr1M/QjaAdLA2gIR0Cuzu287IT5dX2UKGgGR7/YeJ53Tuv2aAdLBGgIR0CuzpDUVi4KdX2UKGgGR7/JKr7wazeGaAdLA2gIR0Cuz1WEbo8qdX2UKGgGR7/X8/2TPjXGaAdLBWgIR0CuzjlEy+HrdX2UKGgGR7+8ZVGTcIqtaAdLAmgIR0Cuz15Grjo7dX2UKGgGR7/a+xW1c+qzaAdLBGgIR0CuzwCJXQt0dX2UKGgGR7/Vsf7rLQokaAdLBGgIR0CuzqM6zVtodX2UKGgGR7+5uNxVAAyVaAdLAmgIR0Cuz2qYqoZRdX2UKGgGR7+zi5uqFRHgaAdLAmgIR0CuzwzxXnyNdX2UKGgGR7/eOEdvKlpHaAdLBGgIR0Cuzk+QMhHLdX2UKGgGR7+7wgDA8B+4aAdLAmgIR0Cuz3Sj59E1dX2UKGgGR7/Qck+otL+QaAdLA2gIR0CuzrTBqKxcdX2UKGgGR7++S7oSteUqaAdLAmgIR0CuzxbvXsgMdX2UKGgGR7/KnmaH9FWoaAdLA2gIR0CuzsRvFWGRdX2UKGgGR7/HWGyon8baaAdLA2gIR0CuzybY02tMdX2UKGgGR7/Yjlgc94eLaAdLBWgIR0Cuzmjlo11odX2UKGgGR7++Dyvs7dSEaAdLAmgIR0Cuzs3HzYmLdX2UKGgGR7+5B8hLXcxkaAdLAmgIR0Cuzy+nhsIndX2UKGgGR7/Q9kjHGS6laAdLA2gIR0Cuzt1uaWondX2UKGgGR7/gChew9q1xaAdLBGgIR0Cuzn01hsqKdX2UKGgGR7/WhKlHjIaMaAdLA2gIR0Cuz0AbZOBUdX2UKGgGR7+is2eg+QlsaAdLAWgIR0CuzoKwIMScdX2UKGgGR7+0KsuFpPAPaAdLAmgIR0CuzoyIpH7QdX2UKGgGR7/SgpSaVlf7aAdLA2gIR0Cuz0+EytV8dX2UKGgGR7/K9/SYw7DEaAdLBGgIR0CuzvJMQEpzdX2UKGgGR7/BaMaS9ugpaAdLAmgIR0Cuzv4ubqhUdX2UKGgGR7/UM10knkT6aAdLA2gIR0Cuzp5GSZBtdX2UKGgGR7/aujh1klNUaAdLBGgIR0Cuz2WJJoTPdX2UKGgGR7/SUrkKeCkHaAdLA2gIR0CuzwysKb8WdX2UKGgGR7/SzuF6AvtdaAdLA2gIR0Cuzqx1HOKPdX2UKGgGR7+8wYcebNKRaAdLAmgIR0Cuz3GwiaAndX2UKGgGR7/PDTBqKxcFaAdLA2gIR0Cuzxx4Y77sdX2UKGgGR7/JTBqKxcFAaAdLA2gIR0Cuz35x7zCldX2UKGgGR7/VPPLPldTpaAdLBGgIR0CuzsB5ooNNdX2UKGgGR7+lZLZi/fwaaAdLAWgIR0CuzsUOd5IIdX2UKGgGR7/Cq5LAYYR/aAdLAmgIR0Cuz4oLgGbDdX2UKGgGR7/I4aP0Zm7KaAdLA2gIR0CuzyyeZof0dX2UKGgGR7+4jMV1wHZ9aAdLAmgIR0CuztCvHLiddX2UKGgGR7/QYBNmDlHSaAdLA2gIR0Cuz5hfrrxBdX2UKGgGR7/XXJYDDCP7aAdLA2gIR0CuzzsU7CBPdX2UKGgGR7+5Mcp9ZzPsaAdLAmgIR0CuztsCtA9ndX2UKGgGR7/CUN8VpKzzaAdLAmgIR0Cuz6U6xPfsdX2UKGgGR7/QHLzPKMefaAdLA2gIR0Cuz0zIvJzUdX2UKGgGR7/Fag2606YFaAdLA2gIR0Cuzu0nG828dX2UKGgGR7/QPiT+vQnhaAdLA2gIR0Cuz7SMtK7JdX2UKGgGR7+3I0ZWJaaDaAdLAmgIR0Cuz1dMTN+tdX2UKGgGR7/AbT+ee4CqaAdLAmgIR0CuzvcQ7LdOdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 77510, "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-5.15.133+-x86_64-with-glibc2.31 # 1 SMP Tue Dec 19 13:14:11 UTC 2023", "Python": "3.10.13", "Stable-Baselines3": "2.1.0", "PyTorch": "2.1.2+cpu", "GPU Enabled": "False", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.0", "OpenAI Gym": "0.26.2"}}