SergioIommi/DQN-2048

Deep Reinforcement Learning to Play 2048 (with Keras)

27
/ 100
Experimental

This project implements an intelligent agent that learns to play the game 2048 using deep reinforcement learning. It takes the current 2048 game board as input and determines the optimal next move (up, down, left, or right) to maximize the score and reach higher tiles. This is designed for researchers and enthusiasts exploring deep reinforcement learning techniques in game environments.

No commits in the last 6 months.

Use this if you are a machine learning researcher or student interested in seeing a practical application of deep Q-networks (DQN) for game playing and wish to experiment with different neural network architectures and training parameters.

Not ideal if you are looking for a pre-trained, ready-to-use bot to simply play 2048 at a high level without engaging in the underlying AI development, or if you prefer traditional game-solving algorithms like expectimax.

reinforcement-learning deep-learning game-AI neural-networks intelligent-agents
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 12 / 25

How are scores calculated?

Stars

28

Forks

4

Language

Python

License

Category

game-ai-solvers

Last pushed

Aug 12, 2018

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SergioIommi/DQN-2048"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.