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: 250.20 +/- 26.64
      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


```python
import gym

from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.

from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env

# Create the environment
env = make_vec_env('LunarLander-v2', n_envs=16)

# Define a PPO MlpPolicy architecture
model = PPO(
    policy = 'MlpPolicy',
    env = env,
    n_steps = 1024,
    batch_size = 64,
    n_epochs = 4,
    gamma = 0.999,
    gae_lambda = 0.98,
    ent_coef = 0.01,
    verbose=1)

# Train the policy for 1,000,000 timesteps
model.learn(total_timesteps=int(1e6))
model_name = "lunar-landing-agent-sid"
model.save(model_name)

# Evaluate policy
# 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,10, True)

# Print the results
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")


# Package to hub
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env

from huggingface_sb3 import package_to_hub

repo_id = "sidraina/ppo-LunarLander-v2"
env_id = "LunarLander-v2"

# Create the evaluation env
eval_env = DummyVecEnv([lambda: gym.make(env_id)])

model_architecture = "PPO"

commit_message = "First PPO LunarLander-v2 trained agent"

# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
package_to_hub(model=model,
               model_name=model_name,
               model_architecture=model_architecture,
               env_id=env_id,
               eval_env=eval_env,
               repo_id=repo_id,
               commit_message=commit_message)
...
```