AI RESEARCH
Extreme Value Monte Carlo Tree Search for Classical Planning
arXiv CS.AI
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ArXi:2405.18248v3 Announce Type: replace Despite being successful in board games and reinforcement learning (RL), Monte Carlo Tree Search (MCTS) combined with Multi Armed Bandits (MABs) has seen limited success in domain-independent classical planning until recently. Previous work (Wissow and Asai 2024) showed that UCB1, designed for bounded rewards, does not perform well as applied to cost-to-go estimates in classical planning, which are unbounded in $\R$, and showed improved performance using a Gaussian reward MAB instead.