  Crazyhouse is a chess variant that incorporates all of the classical chess
rules, but allows users to drop pieces captured from the opponent as a normal
move. Until 2018, all competitive computer engines for this board game made use
of an alpha-beta pruning algorithm with a hand-crafted evaluation function for
each position. Previous machine learning-based algorithms for just regular
chess, such as NeuroChess and Giraffe, took hand-crafted evaluation features as
input rather than a raw board representation. More recent projects, such as
AlphaZero, reached massive success but required massive computational resources
in order to reach its final strength.
  This paper describes the development of SixtyFour, an engine designed to
compete in the chess variant of Crazyhouse with limited hardware. This specific
variant poses a multitude of significant challenges due to its large branching
factor, state-space complexity, and the multiple move types a player can make.
We propose the novel creation of a neural network-based evaluation function for
Crazyhouse. More importantly, we evaluate the effectiveness of an ensemble
model, which allows the training time and datasets to be easily distributed on
regular CPU hardware commodity. Early versions of the network have attained a
playing level comparable to a strong amateur on online servers.
