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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 275.34 +/- 14.56
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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, and huggingface_sb3)
To use this model make sure you are running Python version 3.7.13. You can use [pyenv](https://github.com/pyenv/pyenv) to manage multiple versions of Python on your system.
### Install required packages:
```bash
pip install stable-baselines3
pip install huggingface_sb3
pip install pickle5
pip install Box2D
pip install pyglet
```
You can use this simple script as a base to evaluate and run the model:
```python
import gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from stable_baselines3.common.evaluation import evaluate_policy
# Download the model from the huggingface hub
checkpoint = load_from_hub(
repo_id="kalmufti/PPO-LunarLander-v2",
filename="ppo-LunarLander-v2.zip",
)
# Load the policy
model = PPO.load(checkpoint)
# Create an environment
env = gym.make("LunarLander-v2")
# Optional - evaluate the agent means
mean_reward, std_reward = evaluate_policy(
model, env, render=False, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent playing the environment
obs = env.reset()
for i in range(1000):
action, _state = model.predict(obs)
obs, reward, done, info = env.step(action)
env.render()
if done:
obs = env.reset()
env.close()
``` |