  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.
