Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 237.02 +/- 69.34
+      value: -111.11 +/- 38.22
       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 0x7f8d9a5f53a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d9a5f5430>", "reset_noise": 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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
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+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 10,
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
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-    "_n_updates": 3908,
+    "n_envs": 16,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
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     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 4,
+    "n_epochs": 10,
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ppo-LunarLander-v2/policy.optimizer.pth

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-size 87929
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+size 87545

ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.9.16
-- Stable-Baselines3: 1.7.0
-- PyTorch: 2.0.0+cu118
-- GPU Enabled: True
-- Numpy: 1.22.4
+- OS: Linux-5.15.90+-x86_64-with-debian-bullseye-sid # 1 SMP Thu Apr 6 11:02:12 UTC 2023
+- Python: 3.7.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 1.13.0+cpu
+- GPU Enabled: False
+- Numpy: 1.21.6
 - Gym: 0.21.0

results.json

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-{"mean_reward": 237.0217691572222, "std_reward": 69.33560559556445, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-03T20:46:43.903834"}
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