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---
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: 263.26 +/- 19.25
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
# !pip gymnasium huggingface-sb3 stable_baselines3[extra]
import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
repo_id = "VinayHajare/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
eval_env = gym.make("LunarLander-v2", render_mode="human")
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,print_system_info=True)
mean_reward, std_reward = evaluate_policy(model,eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Enjoy trained agent
observation, info = eval_env.reset()
for _ in range(1000):
action, _states = model.predict(observation, deterministic=True)
observation, rewards, terminated, truncated, info = eval_env.step(action)
eval_env.render()
```
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