Initial commit

README.md

@@ -8,17 +8,16 @@ tags:
 model-index:
 - name: A2C
   results:
-  - task:
+  - metrics:
+    - type: mean_reward
+      value: -1.82 +/- 0.59
+      name: mean_reward
+    task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: PandaReachDense-v2
       type: PandaReachDense-v2
-    metrics:
-    - type: mean_reward
-      value: -0.41 +/- 0.11
-      name: mean_reward
-      verified: false
 ---
 
 # **A2C** Agent playing **PandaReachDense-v2**

a2c-PandaReachDense-v2.zip

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a2c-PandaReachDense-v2/_stable_baselines3_version

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-1.7.0
+1.5.0

a2c-PandaReachDense-v2/data

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+        "__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 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: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the feature 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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         "__abstractmethods__": "frozenset()",
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a2c-PandaReachDense-v2/policy.pth

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a2c-PandaReachDense-v2/system_info.txt

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-- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.8.10
-- Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.1+cu116
-- GPU Enabled: True
-- Numpy: 1.21.6
-- Gym: 0.21.0
+OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 #33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
+Python: 3.9.12
+Stable-Baselines3: 1.5.0
+PyTorch: 1.13.1
+GPU Enabled: False
+Numpy: 1.21.5
+Gym: 0.21.0

config.json

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