Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.

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 networ

SUBMITTER: Czech J 

PROVIDER: S-EPMC7861260 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature