a2c-PandaReachDense-v2 / config.json
aliakyurek's picture
Initial commit
898b33a
{"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 0x7fc31bce1ab0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc31bcdddc0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 175008, "_total_timesteps": 175000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684943408517254612, "learning_rate": 0.00096, "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": "[[ 0.89824337 0.77933073 -0.04509136]\n [-0.35266256 -0.3290277 0.86033547]\n [-0.33779672 -0.00811542 0.652064 ]\n [ 0.96682405 -0.71819884 0.8207724 ]]", "desired_goal": "[[ 0.7620807 1.2462662 -1.2072446 ]\n [-0.42364728 -0.37989712 0.586647 ]\n [-1.5168084 -0.33757934 0.10599089]\n [ 1.2764425 -1.540255 0.6015908 ]]", "observation": "[[ 0.89824337 0.77933073 -0.04509136 -0.7520893 1.1877373 -2.5520146 ]\n [-0.35266256 -0.3290277 0.86033547 -0.29252914 -0.08081523 0.4914936 ]\n [-0.33779672 -0.00811542 0.652064 -0.849577 -0.06053328 -0.07545612]\n [ 0.96682405 -0.71819884 0.8207724 0.50372386 -1.292388 0.16065957]]"}, "_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": "[[-0.00320074 0.02688206 0.05664091]\n [ 0.0280751 -0.09804467 0.05320761]\n [-0.06566316 0.1292425 0.20598876]\n [-0.08837323 -0.04591748 0.15570727]]", "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": -4.571428571420455e-05, "_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": 5469, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}