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.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...