Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search
This solves a specific game-theoretic problem for AI researchers, but it is incremental as it applies known methods to a new domain.
The paper tackles the problem of achieving superhuman performance in Chutes and Ladders by developing AlphaChute, which converges to the Nash equilibrium in constant time and is the first formal solution to this game.
We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nash equilibrium in constant time, and therefore is -- to the best of our knowledge -- the first such formal solution to this game. Surprisingly, despite all this, our implementation of AlphaChute remains relatively straightforward due to domain-specific adaptations. We provide the source code for AlphaChute here in our Appendix.