Information-guided Planning: An Online Approach for Partially Observable Problems

Matheus Aparecido Do Carmo Alves, Amokh Varma, Yehia Elkhatib, Leandro Soriano Marcolino

Keywords: Information-guided planning, Planning under uncertainty, Sequential decision making

TL;DR: IB-POMCP is a novel algorithm that performs an online and information-guided planning process under uncertainty to solve sequential decison making problems.

Abstract: This paper presents IB-POMCP, a novel algorithm for online planning under partial observability. Our approach enhances the decision-making process by using estimations of the world belief's entropy to guide a tree search process and surpass the limitations of planning in scenarios with sparse reward configurations. By performing what we denominate as an *information-guided planning process*, the algorithm, which incorporates a novel I-UCB function, shows significant improvements in reward and reasoning time compared to state-of-the-art baselines in several benchmark scenarios, along with theoretical convergence guarantees.

Submission Number: 8692