Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Sprache des Titels:
Englisch
Original Kurzfassung:
Deep neural networks have been successfully applied in learning the board games Go, chess
and shogi without prior knowledge by making use of reinforcement learning. Although starting
from zero knowledge has been shown to yield impressive results, it is associated with high
computationally costs especially for complex games. With this paper, we present CrazyAra which
is a neural network based engine solely trained in supervised manner for the chess variant
crazyhouse. Crazyhouse is a game with a higher branching factor than chess and there is only
limited data of lower quality available compared to AlphaGo. Therefore, we focus on improving
efficiency in multiple aspects while relying on low computational resources. These improvements
include modifications in the neural network design and training configuration, the introduction
of a data normalization step and a more sample efficient Monte-Carlo tree search which has a
lower chance to blunder. After training on 569,537 human games for 1.5 days we achieve a move
prediction accuracy of 60.4 %. During development, versions of CrazyAra played professional
human players. Most notably, CrazyAra achieved a four to one win over 2017 crazyhouse world
champion Justin Tan (aka LM Jann Lee) who is more than 400 Elo higher rated compared to
the average player in our training set. Furthermore, we test the playing strength of CrazyAra
on CPU against all participants of the second Crazyhouse Computer Championships 2017,
winning against twelve of the thirteen participants. Finally, for CrazyAraFish we continue training
our model on generated engine games. In ten long-time control matches playing Stockfish 10,
CrazyAraFish wins three games and draws one out of ten matches.
Forschungs-