2018/06/02 by G. Vijay Krishna, Sai Krishna G. V., Kyle Goyette +6 · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Artificial intelligence #Code (set theory) #Computer chess #Computer science #FOS: Computer and information sciences #Field (mathematics) #Mathematics #Programming language #Reinforcement Learning in Robotics #Reinforcement learning #Sports Analytics and Performance #cs.AI
paper · pdf · doi:10.48550/arxiv.1806.00683
published in arXiv (Cornell University) (Cornell University) · Tabula Rasa, Chess engine, Learning Fast and Slow, Reinforcement Learning, Alpha Zero
openalex publication_date 2018/06/02 · openalex created_date 2018/06/13 · arxiv created 2018/10/17 · arxiv updated 2018/10/19 · openalex updated_date 2026/08/05
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.