Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,51 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 265.23 +/- 17.90
+      value: 278.45 +/- 19.35
       name: mean_reward
       verified: false
 ---
 
 # **PPO** Agent playing **LunarLander-v2**
-A trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
+TODO: Add your code
 
-```python
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3.common.evaluation import evaluate_policy
 
+```python
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
-
-# Download the model checkpoint
-model_checkpoint = load_from_hub("ckandemir/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip")
-# Create a vectorized environment
-env = make_vec_env("LunarLander-v2", n_envs=1)
-
-# Load the model
-model = PPO.load(model_checkpoint, env=env)
-
-# Evaluate
-print("Evaluating model")
-mean_reward, std_reward = evaluate_policy(
-    model,
-    env,
-    n_eval_episodes=10,
-    deterministic=True,
-)
-print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}")
-
-# Start a new episode
-obs = env.reset()
-
-try:
-    while True:
-        action, state = model.predict(obs, deterministic=True)
-        obs, reward, done, info = env.step(action)
-        env.render()
-
-except KeyboardInterrupt:
-    pass
+...
 ```

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 0x7e23eca05900>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e23eca05990>", "reset_noise": 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 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.0.1+cu118
-- GPU Enabled: False
-- Numpy: 1.25.2
+- PyTorch: 2.3.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 265.2337943740299, "std_reward": 17.89570000290142, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-10T07:09:05.900548"}
+{"mean_reward": 278.45308480000006, "std_reward": 19.3480932406175, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-19T19:12:09.118495"}