3 research outputs found
Lookahead Pathology in Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) is an adversarial search paradigm that first
found prominence with its success in the domain of computer Go. Early
theoretical work established the game-theoretic soundness and convergence
bounds for Upper Confidence bounds applied to Trees (UCT), the most popular
instantiation of MCTS; however, there remain notable gaps in our understanding
of how UCT behaves in practice. In this work, we address one such gap by
considering the question of whether UCT can exhibit lookahead pathology -- a
paradoxical phenomenon first observed in Minimax search where greater search
effort leads to worse decision-making. We introduce a novel family of synthetic
games that offer rich modeling possibilities while remaining amenable to
mathematical analysis. Our theoretical and experimental results suggest that
UCT is indeed susceptible to pathological behavior in a range of games drawn
from this family