LEARNING TO PLAY THE CHESS VARIANT CRAZYHOUSE ABOVE WORLD CHAMPION LEVEL WITH DEEP NEURAL NETWORKS AND HUMAN DATA APREPRINT Johannes Czech 1;*, Moritz Willig 1, Alena Beyer 1, Kristian Kersting 1;2, Johannes Fürnkranz 1 1 Department of Computer Science, TU Darmstadt, Germany 2 Centre for Cognitive Science, TU Darmstadt, Germany Correspondence:*
[email protected] August 23, 2019 ABSTRACT 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.