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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
name: mean_reward
value: 288.92 +/- 21.79
verified: false
---
# 🚀 PPO Agent for LunarLander-v2
This is a trained **PPO agent** for the **LunarLander-v2** environment using Stable-Baselines3.
## Developer
**Vishand S (@Vishand03)**
## Frameworks
- Stable-Baselines3
- PyTorch
## Training Details
- Algorithm: PPO
- Timesteps: 2.5M
- Mean Reward: ~288.9
- Discount factor (γ): 0.99
- Learning rate: 3e-4
- Optimizer: Adam
---
## 🎥 Demo (Preview)
---
## 🎬 Full Demo Video
👉 [Watch the full video here](replay.mp4)
---
## 🛠 Usage
```python
import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_hub import hf_hub_download
# -------------------------
# Environment Setup
# -------------------------
env = gym.make("LunarLander-v2", render_mode="human") # Human render
eval_env = Monitor(gym.make("LunarLander-v2")) # Evaluation (no render)
# -------------------------
# Load pretrained model
# -------------------------
model_path = hf_hub_download("Vishand03/lunarlander-ppo", "model.zip")
model = PPO.load(model_path)
# -------------------------
# Run one episode
# -------------------------
obs, _ = env.reset()
done = False
while not done:
action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, _ = env.step(action)
done = terminated or truncated
# -------------------------
# Evaluate policy
# -------------------------
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"Mean Reward: {mean_reward:.2f} +/- {std_reward:.2f}")
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