anh-nn01/Lunar-Lander-Double-Deep-Q-Networks
An AI agent that use Double Deep Q-learning to teach itself to land a Lunar Lander on OpenAI universe
This project offers an AI agent that teaches itself how to land a virtual lunar lander safely and quickly on a designated landing pad. By observing its actions and receiving feedback on its performance, the agent learns to control the lander's thrusters. This tool is designed for AI researchers and enthusiasts who are exploring reinforcement learning techniques.
No commits in the last 6 months.
Use this if you are studying or experimenting with reinforcement learning and want to see how a Double Deep Q-Network can solve a control problem like landing a spacecraft.
Not ideal if you are looking for a practical tool to control real-world spacecraft or for a simple simulation without deep learning.
Stars
17
Forks
6
Language
Python
License
MIT
Category
Last pushed
Mar 15, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
vietnh1009/Super-mario-bros-PPO-pytorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
taherfattahi/ppo-rocket-landing
Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket...
Itomigna2/Muesli-lunarlander
Muesli RL algorithm implementation (PyTorch) (LunarLander-v2)
fvalka/atc-reinforcement-learning
Reinforcement learning for an air traffic control task. OpenAI gym based simulation.
ugurcanozalp/heli-gym
OpenAI GYM environment for 6-DOF Helicopter simulation