  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.
