---
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: 271.39 +/- 12.49
      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)
TODO: Add your code

# Create environment
env = gym.make('LunarLander-v2')
# Instantiate the agent
model = PPO('MlpPolicy', env, verbose=1)
# Train the agent
model.learn(total_timesteps=int(2e5))

# TODO: Define a PPO MlpPolicy architecture
# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
# if we had frames as input we would use CnnPolicy
model = PPO(
    policy = 'MlpPolicy',
    env = env,
    n_steps = 4096,
    batch_size = 128,
    n_epochs = 8,
    gamma = 0.999,
    gae_lambda = 0.98,
    ent_coef = 0.01,
    verbose=1)

# TODO: Train it for 1,000,000 timesteps
model.learn(total_timesteps=2000000)
# TODO: Specify file name for model and save the model to file
model_name = "ppo-LunarLander-v2"
model.save(model_name)

# TODO: Evaluate the agent
# Create a new environment for evaluation
eval_env = Monitor(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}")


```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

...
```