First attempt, 500k steps

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 262.59 +/- 13.90
+      value: 262.87 +/- 24.14
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7d915539b9c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d915539ba60>", "reset_noise": 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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

@@ -1,9 +1,9 @@
-- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
-- Python: 3.11.11
+- OS: Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025
+- Python: 3.11.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.5.1+cu121
+- PyTorch: 2.6.0+cu124
 - GPU Enabled: True
-- Numpy: 1.26.4
-- Cloudpickle: 3.1.0
+- Numpy: 2.0.2
+- Cloudpickle: 3.1.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 262.5920689, "std_reward": 13.899024713835164, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-01-24T09:19:56.619257"}
+{"mean_reward": 262.8682792, "std_reward": 24.143141659742305, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-04-24T12:44:29.580170"}