  Recent progress in reinforcement learning (RL) using self-game-play has shown
remarkable performance on several board games (e.g., Chess and Go) as well as
video games (e.g., Atari games and Dota2). It is plausible to consider that RL,
starting from zero knowledge, might be able to gradually approximate a winning
strategy after a certain amount of training. In this paper, we explore neural
Monte-Carlo-Tree-Search (neural MCTS), an RL algorithm which has been applied
successfully by DeepMind to play Go and Chess at a super-human level. We try to
leverage the computational power of neural MCTS to solve a class of
combinatorial optimization problems. Following the idea of Hintikka's
Game-Theoretical Semantics, we propose the Zermelo Gamification (ZG) to
transform specific combinatorial optimization problems into Zermelo games whose
winning strategies correspond to the solutions of the original optimization
problem. The ZG also provides a specially designed neural MCTS. We use a
combinatorial planning problem for which the ground-truth policy is efficiently
computable to demonstrate that ZG is promising.

Artificial Intelligence;Machine Learning