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Mohr, Felix ORCID logoORCID: https://orcid.org/0000-0002-9293-2424; Bengs, Viktor and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (February 2021): Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI Conference on Artificial Intelligence, Virtual, February 2–9, 2021. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35, No. 14 pp. 12373-12381

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Abstract

The problem of minimal cost path search is especially difficult when no useful heuristics are available. A common solution is roll-out-based search like Monte Carlo Tree Search (MCTS). However, MCTS is mostly used in stochastic or adversarial environments, with the goal to identify an agent's best next move. For this reason, even though single player versions of MCTS exist, most algorithms, including UCT, are not directly tailored to classical minimal cost path search. We present Plackett-Luce MCTS (PL-MCTS), a path search algorithm based on a probabilistic model over the qualities of successor nodes. We empirically show that PL-MCTS is competitive and often superior to the state of the art.

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