A scalable neural network architecture for board games
Tom Schaul, Jürgen Schmidhuber · 2008
This paper proposes to use multi-dimensional recurrent neural networks (MDRNNs) as a way to overcome one of the key problems in flexible-size board games: scalability. We show why this architecture is well suited to the domain and how it can be successfully trained to play those games, even without any domain-specific knowledge. We find that performance on small boards correlates well with performance on large ones, and that this property holds for networks trained by either evolution or coevolution.