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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: 266.93 +/- 24.72
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)
```python
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
# Create a vectorized environment of 64 parallel environments
env = make_vec_env("LunarLander-v2", n_envs=64)
# Optimizaed Hyperparameters
model = PPO(
"MlpPolicy",
env=env,
n_steps=1024,
batch_size=32,
n_epochs=10,
gamma=0.997,
gae_lambda=0.98,
ent_coef=0.01,
verbose=1,
)
# Train it for 1,000,000 timesteps
model.learn(total_timesteps=int(1e6))
# Create a new environment for evaluation
eval_env = gym.make("LunarLander-v2")
# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(
model, eval_env, n_eval_episodes=10, deterministic=True
)
# Print the results
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# >>> mean_reward=261.42 +/- 18.69168514436243
```
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