a2c-PandaReachDense-v2 / config.json
PhysHunter's picture
Initial commit
8079246
{"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 0x7f9f54ba9fc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9f54ba6200>"}, "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": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1686398220263915513, "learning_rate": 0.001, "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": "[[ 0.40148082 -0.00779387 0.5453027 ]\n [ 0.40148082 -0.00779387 0.5453027 ]\n [ 0.40148082 -0.00779387 0.5453027 ]\n [ 0.40148082 -0.00779387 0.5453027 ]]", "desired_goal": "[[-0.6355903 -0.32457063 -0.9123001 ]\n [ 1.5488495 0.14332478 1.0921398 ]\n [-0.34929642 -0.27591467 0.64136237]\n [ 0.9744933 1.1049606 1.377286 ]]", "observation": "[[ 4.0148082e-01 -7.7938652e-03 5.4530269e-01 -1.7178323e-02\n -4.4150031e-04 -9.8520974e-03]\n [ 4.0148082e-01 -7.7938652e-03 5.4530269e-01 -1.7178323e-02\n -4.4150031e-04 -9.8520974e-03]\n [ 4.0148082e-01 -7.7938652e-03 5.4530269e-01 -1.7178323e-02\n -4.4150031e-04 -9.8520974e-03]\n [ 4.0148082e-01 -7.7938652e-03 5.4530269e-01 -1.7178323e-02\n -4.4150031e-04 -9.8520974e-03]]"}, "_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": "[[-0.10068887 -0.03504529 0.26072362]\n [ 0.0238905 0.03511719 0.05718637]\n [-0.05659797 -0.11357039 0.21752955]\n [ 0.09287077 0.10589149 0.16817759]]", "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": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIDmYTYFh+/L+UhpRSlIwBbJRLMowBdJRHQLWWU+TNdJJ1fZQoaAZoCWgPQwhUVWgglk31v5SGlFKUaBVLMmgWR0C1lhzCk43ndX2UKGgGaAloD0MIwvf+Bu3V9b+UhpRSlGgVSzJoFkdAtZXfNliBoXV9lChoBmgJaA9DCHe/CvDdJve/lIaUUpRoFUsyaBZHQLWVoh24d6t1fZQoaAZoCWgPQwjEYP4Kmav7v5SGlFKUaBVLMmgWR0C1ly4egctHdX2UKGgGaAloD0MIyJdQweEF+L+UhpRSlGgVSzJoFkdAtZb2/7BO6HV9lChoBmgJaA9DCJfHmpFBLvu/lIaUUpRoFUsyaBZHQLWWuYq5LAZ1fZQoaAZoCWgPQwgrNBDLZs72v5SGlFKUaBVLMmgWR0C1lnzLW7OFdX2UKGgGaAloD0MIhhvw+WHE+r+UhpRSlGgVSzJoFkdAtZfyaLGaQXV9lChoBmgJaA9DCIjaNoyCIPi/lIaUUpRoFUsyaBZHQLWXu1EVnEl1fZQoaAZoCWgPQwj0+L1Nfzb7v5SGlFKUaBVLMmgWR0C1l33EZR8/dX2UKGgGaAloD0MI+P9xwoQR+7+UhpRSlGgVSzJoFkdAtZdAoQWepXV9lChoBmgJaA9DCCqsVFBRtf6/lIaUUpRoFUsyaBZHQLWYtYKYzBR1fZQoaAZoCWgPQwiz8PW1LrXzv5SGlFKUaBVLMmgWR0C1mH5aePJadX2UKGgGaAloD0MIyCO4kbIF+b+UhpRSlGgVSzJoFkdAtZhAxZdOZnV9lChoBmgJaA9DCOoj8IefP/y/lIaUUpRoFUsyaBZHQLWYA6nBLwp1fZQoaAZoCWgPQwg9RQ4RN2cCwJSGlFKUaBVLMmgWR0C1mTXYHxBmdX2UKGgGaAloD0MI3CvzVl3H+r+UhpRSlGgVSzJoFkdAtZj+Z8a4t3V9lChoBmgJaA9DCN6q61BNSfe/lIaUUpRoFUsyaBZHQLWYwIUahpR1fZQoaAZoCWgPQwi3fY/665UAwJSGlFKUaBVLMmgWR0C1mIMMI/qxdX2UKGgGaAloD0MIdO0L6IX7/b+UhpRSlGgVSzJoFkdAtZmwLux8lXV9lChoBmgJaA9DCGaIY13cRve/lIaUUpRoFUsyaBZHQLWZeL/jsD51fZQoaAZoCWgPQwi6TE2CN+T7v5SGlFKUaBVLMmgWR0C1mTrmMfihdX2UKGgGaAloD0MIRwINNnVe9r+UhpRSlGgVSzJoFkdAtZj9rVOKwnV9lChoBmgJaA9DCPeTMT7MXv2/lIaUUpRoFUsyaBZHQLWaL9du5z51fZQoaAZoCWgPQwiL/WX35CH4v5SGlFKUaBVLMmgWR0C1mfh9XtBwdX2UKGgGaAloD0MIRaFl3T/W8r+UhpRSlGgVSzJoFkdAtZm6mdiDunV9lChoBmgJaA9DCAA3ixcLg/i/lIaUUpRoFUsyaBZHQLWZfSQYDT11fZQoaAZoCWgPQwjpLLMIxRb3v5SGlFKUaBVLMmgWR0C1mqL8rI5pdX2UKGgGaAloD0MIucFQhxVu+b+UhpRSlGgVSzJoFkdAtZprgydnTXV9lChoBmgJaA9DCAXfNH12QPm/lIaUUpRoFUsyaBZHQLWaLZ+hGpd1fZQoaAZoCWgPQwgP8Q9berT1v5SGlFKUaBVLMmgWR0C1mfAlOXVtdX2UKGgGaAloD0MIkpVfBmPEAMCUhpRSlGgVSzJoFkdAtZsdGsmv4nV9lChoBmgJaA9DCD4HliNkIPW/lIaUUpRoFUsyaBZHQLWa5asIVud1fZQoaAZoCWgPQwgg1bDfEyv9v5SGlFKUaBVLMmgWR0C1mqfGZNO/dX2UKGgGaAloD0MIEd+JWS9G9r+UhpRSlGgVSzJoFkdAtZpqbXpW3nV9lChoBmgJaA9DCLCPTl35LPm/lIaUUpRoFUsyaBZHQLWblLeANG51fZQoaAZoCWgPQwhSf73Cgrv7v5SGlFKUaBVLMmgWR0C1m11FQVKxdX2UKGgGaAloD0MI1VqYhXYO+b+UhpRSlGgVSzJoFkdAtZsfZK3/gnV9lChoBmgJaA9DCE8iwr8IGvy/lIaUUpRoFUsyaBZHQLWa4e0G/vh1fZQoaAZoCWgPQwhPQBNhw1MFwJSGlFKUaBVLMmgWR0C1nAyZ8a4udX2UKGgGaAloD0MIeCl1yTgmAMCUhpRSlGgVSzJoFkdAtZvVJBgNPXV9lChoBmgJaA9DCNpzmZoE7/2/lIaUUpRoFUsyaBZHQLWbl0PYnOV1fZQoaAZoCWgPQwjItgw4S4n8v5SGlFKUaBVLMmgWR0C1m1niJfpmdX2UKGgGaAloD0MIYHR5c7hW+L+UhpRSlGgVSzJoFkdAtZyAh5gPVnV9lChoBmgJaA9DCLZKsDic+fa/lIaUUpRoFUsyaBZHQLWcSRQrMC91fZQoaAZoCWgPQwhzDp4JTRL3v5SGlFKUaBVLMmgWR0C1nAsv24/edX2UKGgGaAloD0MI1o13R8bq/b+UhpRSlGgVSzJoFkdAtZvNvxYq5XV9lChoBmgJaA9DCKWD9X8O8/6/lIaUUpRoFUsyaBZHQLWc9f8/D+B1fZQoaAZoCWgPQwjXpNsSuWAAwJSGlFKUaBVLMmgWR0C1nL6wdKdydX2UKGgGaAloD0MIRL5LqUvG+b+UhpRSlGgVSzJoFkdAtZyAzJp35nV9lChoBmgJaA9DCBb59UNs8PK/lIaUUpRoFUsyaBZHQLWcQ1eBxxV1fZQoaAZoCWgPQwiWQiCXOPL+v5SGlFKUaBVLMmgWR0C1nXA7cO9WdX2UKGgGaAloD0MI+l5DcFxG/L+UhpRSlGgVSzJoFkdAtZ04xk/bCnV9lChoBmgJaA9DCPoLPWL03Pi/lIaUUpRoFUsyaBZHQLWc+uFYdQx1fZQoaAZoCWgPQwh4JjRJLAkAwJSGlFKUaBVLMmgWR0C1nL1pwjt5dX2UKGgGaAloD0MIXqCkwALY/b+UhpRSlGgVSzJoFkdAtZ3kornTzHV9lChoBmgJaA9DCHhCrz+Jz/y/lIaUUpRoFUsyaBZHQLWdrSyMUAV1fZQoaAZoCWgPQwh3+GuyRj35v5SGlFKUaBVLMmgWR0C1nW9LQHAzdX2UKGgGaAloD0MIwMx38BOH+b+UhpRSlGgVSzJoFkdAtZ0x9x6v7nV9lChoBmgJaA9DCN3T1R2Lbfu/lIaUUpRoFUsyaBZHQLWeXRrJr+J1fZQoaAZoCWgPQwh65A8Gnrv5v5SGlFKUaBVLMmgWR0C1niWzByjpdX2UKGgGaAloD0MIGlJF8Sqr87+UhpRSlGgVSzJoFkdAtZ3n5XU6P3V9lChoBmgJaA9DCBJKXwg5L/a/lIaUUpRoFUsyaBZHQLWdqo4dZJV1fZQoaAZoCWgPQwgv/UtSmSL7v5SGlFKUaBVLMmgWR0C1ntVR51NhdX2UKGgGaAloD0MIWdsUj4uq/L+UhpRSlGgVSzJoFkdAtZ6d2t+1B3V9lChoBmgJaA9DCHu+ZrlsdP2/lIaUUpRoFUsyaBZHQLWeX/Tb3491fZQoaAZoCWgPQwhhcM0d/e/0v5SGlFKUaBVLMmgWR0C1niJ3PiT/dX2UKGgGaAloD0MIL8N/uoHC+L+UhpRSlGgVSzJoFkdAtZ9QSf16FHV9lChoBmgJaA9DCEdZv5mYrv2/lIaUUpRoFUsyaBZHQLWfGNnXd0t1fZQoaAZoCWgPQwgbS1gbY2f3v5SGlFKUaBVLMmgWR0C1ntsbBGhFdX2UKGgGaAloD0MIyJV6FoSy+7+UhpRSlGgVSzJoFkdAtZ6dn/T9bXV9lChoBmgJaA9DCM3K9iFvefW/lIaUUpRoFUsyaBZHQLWfwog3cYZ1fZQoaAZoCWgPQwgvw3+6gUL4v5SGlFKUaBVLMmgWR0C1n4sSbpeNdX2UKGgGaAloD0MIuRluwOdH+r+UhpRSlGgVSzJoFkdAtZ9NZPl+3HV9lChoBmgJaA9DCPDAAMKHkvq/lIaUUpRoFUsyaBZHQLWfD+36Q/51fZQoaAZoCWgPQwiQwB9+/psCwJSGlFKUaBVLMmgWR0C1oDuyNXHSdX2UKGgGaAloD0MI8aDZdW+F9r+UhpRSlGgVSzJoFkdAtaAEQ6IWQHV9lChoBmgJaA9DCLQfKSLDqva/lIaUUpRoFUsyaBZHQLWfxmG/N7l1fZQoaAZoCWgPQwhdGVQbnIj+v5SGlFKUaBVLMmgWR0C1n4jvE0iydX2UKGgGaAloD0MIa2RXWkaq+L+UhpRSlGgVSzJoFkdAtaCs0waisXV9lChoBmgJaA9DCLaizXFu0/y/lIaUUpRoFUsyaBZHQLWgdZiuuA91fZQoaAZoCWgPQwj0b5f9upP3v5SGlFKUaBVLMmgWR0C1oDfSpiqidX2UKGgGaAloD0MI9aEL6lsm9b+UhpRSlGgVSzJoFkdAtZ/6Y7aIvnV9lChoBmgJaA9DCJdyvth78f6/lIaUUpRoFUsyaBZHQLWhJUrkKeF1fZQoaAZoCWgPQwgqAMYzaCj5v5SGlFKUaBVLMmgWR0C1oO3T/hl2dX2UKGgGaAloD0MIwFsgQfEj9L+UhpRSlGgVSzJoFkdAtaCv7WNFSnV9lChoBmgJaA9DCFwFMdC17/q/lIaUUpRoFUsyaBZHQLWgcnxaxHJ1fZQoaAZoCWgPQwhTXFX2XVH0v5SGlFKUaBVLMmgWR0C1oZm4I8hcdX2UKGgGaAloD0MIADyiQnUz9L+UhpRSlGgVSzJoFkdAtaFiWa+ev3V9lChoBmgJaA9DCFbUYBqGD/e/lIaUUpRoFUsyaBZHQLWhJHi3ocJ1fZQoaAZoCWgPQwgrhxbZzvf6v5SGlFKUaBVLMmgWR0C1oOcCgbqAdX2UKGgGaAloD0MIZf1mYrqQ+L+UhpRSlGgVSzJoFkdAtaIUQNCqqHV9lChoBmgJaA9DCHhGW5VEdvi/lIaUUpRoFUsyaBZHQLWh3MkyDZl1fZQoaAZoCWgPQwhCtFa0OY77v5SGlFKUaBVLMmgWR0C1oZ7oKUmldX2UKGgGaAloD0MIWwuz0M7p+7+UhpRSlGgVSzJoFkdAtaFhbpu/DnV9lChoBmgJaA9DCIZVvJF55Pa/lIaUUpRoFUsyaBZHQLWihs5n14B1fZQoaAZoCWgPQwhz8bc9QSL2v5SGlFKUaBVLMmgWR0C1ok9bC79RdX2UKGgGaAloD0MIWJHRAUkY97+UhpRSlGgVSzJoFkdAtaIRd1MdtHV9lChoBmgJaA9DCCGSIcfWM/G/lIaUUpRoFUsyaBZHQLWh0/zasZJ1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Gym": "0.21.0"}}