---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- gymnasium
model-index:
- name: PPO
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v2
      type: LunarLander-v2
    metrics:
    - type: mean_reward
      value: 264.37 +/- 27.14
      name: mean_reward
      verified: false
license: mit
language:
- en
pipeline_tag: reinforcement-learning
---

# **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). 
It also represents my first attempt to effectively train a RL agent using **StableBaselines3** and **Gymnasium**, 
done during the 🤗 Deep Reinforcement Learning Course.

## Usage (with Stable-baselines3)



```python
import gymnasium as gym

from huggingface_sb3 import load_from_hub

from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.evaluation import evaluate_policy


repo_id = "Mattizza/PPO-LunarLander-v2_v0__DeepRLCourse"
filename = "ppo-LunarLander-v2_v0.zip"

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, print_system_info=True)

# Evaluate the agent
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
```