README.md

---
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)

![LunarLander](lunarlander.gif)

---

## 🎬 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}")