---
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
      value: 275.80 +/- 20.96
      name: mean_reward
      verified: false
---

# **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).

## Usage (with Stable-baselines3)

Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:

```
pip install stable-baselines3
pip install huggingface_sb3
```
Then, you can use the model like this:

```python
import gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

checkpoint = load_from_hub(repo_id="Felipe474/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)

# Evaluate the agent
eval_env = 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}")

# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = eval_env.step(action)
    eval_env.render()
    if done:
        obs = eval_env.reset()
eval_env.close()