Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting
Artificial Intelligence
2018-10-19 v2
Abstract
An almost-perfect chess playing agent has been a long standing challenge in the field of Artificial Intelligence. Some of the recent advances demonstrate we are approaching that goal. In this project, we provide methods for faster training of self-play style algorithms, mathematical details of the algorithm used, various potential future directions, and discuss most of the relevant work in the area of computer chess. Deep Pepper uses embedded knowledge to accelerate the training of the chess engine over a "tabula rasa" system such as Alpha Zero. We also release our code to promote further research.
Keywords
Cite
@article{arxiv.1806.00683,
title = {Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting},
author = {Sai Krishna G. V. and Kyle Goyette and Ahmad Chamseddine and Breandan Considine},
journal= {arXiv preprint arXiv:1806.00683},
year = {2018}
}
Comments
Tabula Rasa, Chess engine, Learning Fast and Slow, Reinforcement Learning, Alpha Zero