README.md

---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - metrics:
    - type: mean_reward
      value: 256.40 +/- 21.37
      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 16 parallel environments
env = make_vec_env("LunarLander-v2", n_envs=16)

# Optimizaed Hyperparameters
model = PPO(
    "MlpPolicy",
    env=env,
    n_steps=655,
    batch_size=32,
    n_epochs=8,
    gamma=0.998,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
)

# Train it for 500,000 timesteps
model.learn(total_timesteps=int(5e6))

# 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=254.56 +/- 18.45056958672337


  ```