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
      value: 289.96 +/- 22.59
      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)


```python
import gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

checkpoint = load_from_hub(
	repo_id="dmenini/ppo-LunarLander-v2",
	filename="ppo-LunarLander-v2.zip"
)

model = PPO.load(checkpoint)

env = gym.make("LunarLander-v2")

# Evaluate the agent and watch it
eval_env = gym.make("LunarLander-v2")
mean_reward, std_reward = evaluate_policy(
    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

```