Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 233.30 +/- 154.48
+      value: 319.91 +/- 45.37
       name: mean_reward
     task:
       type: reinforcement-learning

a2c-AntBulletEnv-v0.zip

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a2c-AntBulletEnv-v0/data

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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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    ",
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a2c-AntBulletEnv-v0/policy.optimizer.pth

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a2c-AntBulletEnv-v0/system_info.txt

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-OS: Darwin-19.6.0-x86_64-i386-64bit Darwin Kernel Version 19.6.0: Tue Feb 15 21:39:11 PST 2022; root:xnu-6153.141.59~1/RELEASE_X86_64
-Python: 3.7.6
+OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.27 #138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022
+Python: 3.9.12
 Stable-Baselines3: 1.6.0
-PyTorch: 1.11.0
-GPU Enabled: False
-Numpy: 1.21.5
+PyTorch: 1.12.0+cu102
+GPU Enabled: True
+Numpy: 1.23.1
 Gym: 0.21.0

config.json

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