Very short training (80k steps?)

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -3.26 +/- 0.63
+      value: -1.38 +/- 0.26
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

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+size 107972

a2c-PandaReachDense-v2/data

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+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x0000026772408430>",
         "__abstractmethods__": "frozenset()",
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     "verbose": 1,
     "policy_kwargs": {
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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 50000,
+    "_n_updates": 4962,
     "n_steps": 5,
     "gamma": 0.99,
     "gae_lambda": 1.0,

a2c-PandaReachDense-v2/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
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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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
-- Python: 3.10.6
+- OS: Windows-10-10.0.19045-SP0 10.0.19045
+- Python: 3.10.7
 - Stable-Baselines3: 1.8.0
-- PyTorch: 2.0.1+cu118
+- PyTorch: 2.0.1+cu117
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.24.3
 - Gym: 0.21.0

config.json

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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 0x7c0e571a91b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c0e571b6900>"}, "verbose": 1, 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results.json

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