Monte Carlo Tree Search guided by Symbolic Advice for MDPs

TL;DR

This paper introduces an enhanced Monte Carlo Tree Search algorithm for Markov decision processes that incorporates symbolic advice via QBF and SAT solvers, improving performance in complex games like Pac-Man.

Contribution

It presents a novel method of integrating symbolic advice into MCTS while maintaining theoretical guarantees, demonstrated through practical game experiments.

Findings

Enhanced MCTS outperforms standard MCTS in Pac-Man.

Symbolic advice improves decision-making efficiency.

Algorithm maintains theoretical guarantees of classical MCTS.

Abstract

In this paper, we consider the online computation of a strategy that aims at optimizing the expected average reward in a Markov decision process. The strategy is computed with a receding horizon and using Monte Carlo tree search (MCTS). We augment the MCTS algorithm with the notion of symbolic advice, and show that its classical theoretical guarantees are maintained. Symbolic advice are used to bias the selection and simulation strategies of MCTS. We describe how to use QBF and SAT solvers to implement symbolic advice in an efficient way. We illustrate our new algorithm using the popular game Pac-Man and show that the performances of our algorithm exceed those of plain MCTS as well as the performances of human players.