  Game solving is a similar, yet more difficult task than mastering a game.
Solving a game typically means to find the game-theoretic value (outcome given
optimal play), and optionally a full strategy to follow in order to achieve
that outcome. The AlphaZero algorithm has demonstrated super-human level play,
and its powerful policy and value predictions have also served as heuristics in
game solving. However, to solve a game and obtain a full strategy, a winning
response must be found for all possible moves by the losing player. This
includes very poor lines of play from the losing side, for which the AlphaZero
self-play process will not encounter. AlphaZero-based heuristics can be highly
inaccurate when evaluating these out-of-distribution positions, which occur
throughout the entire search. To address this issue, this paper investigates
applying online fine-tuning while searching and proposes two methods to learn
tailor-designed heuristics for game solving. Our experiments show that using
online fine-tuning can solve a series of challenging 7x7 Killall-Go problems,
using only 23.54% of computation time compared to the baseline without online
fine-tuning. Results suggest that the savings scale with problem size. Our
method can further be extended to any tree search algorithm for problem
solving. Our code is available at
https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.
