Upload PPO LunarLander-v2 trained baseline agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 265.63 +/- 17.94
+      value: 288.98 +/- 14.11
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -23,6 +23,7 @@ model-index:
 
 
 
+
 # **PPO** Agent playing **LunarLander-v2**
 This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

config.json

@@ -1 +1 @@
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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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x000002A43A237940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000002A43A2379D0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000002A43A237A60>", "_build_mlp_extractor": "<function 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10.0.22000", "Python": "3.9.7", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.22.3", "Gym": "0.21.0"}}
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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. 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ppo3-LunarLander-v2/_stable_baselines3_version

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ppo3-LunarLander-v2/data

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+Python: 3.9.7
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.22.3
+Gym: 0.21.0

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4c7397e7384f313b7faf450f5351ff81d32deda5f0f974051b8fec8f219db07e
-size 213517
+oid sha256:438594a100465adc45bf631b08e470ca45328f3fe042fc5be539e555b09307d3
+size 193915

results.json

@@ -1 +1 @@
-{"mean_reward": 265.63357266940875, "std_reward": 17.94413586357526, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-28T21:14:07.570970"}
+{"mean_reward": 288.97849806711315, "std_reward": 14.110054148436564, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-28T22:28:04.909956"}