---
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: 260.63 +/- 20.34
      name: mean_reward
      verified: false
---

Today I landed my Lunar Lander on the moon using reinforcement learning 🌙

Reinforcement Learning is a computational approach of learning from actions. 

Instead of manually controlling the inputs and directions of the thrusters, I used a Proximal Policy Optimization agent that is a mix of value-based RL and policy-based RL to predict actions leading to the greatest sum of rewards. https://huggingface.co/AGI-CEO/ppo-LunarLander-v2

I am learning this as part of  Hugging Face's Deep RL Course (https://huggingface.co/learn/deep-rl-course/). It's a free, self-paced course.

I am planning on hosting group study sessions and create a free, open cohort to learn Deep Learning over the next 10 weeks.

I will host these study sessions and discussions on my AI Engineering / Entrepreneurship Discord group with 500+ members.  

If you have some experience with Python and are interested in joining a group to learn Deep RL free of charge, please reach out to me. I am planning the first group session next Thursday at 10AM Eastern.

# **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
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

...
```