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

do Carmo Alves, M. A., Varma, A., Elkhatib, Y. and Marcolino, L. S. (2023) Information-Guided Planning: An Online Approach for Partially Observable Problems. In: NeurIPS 2023, New Orleans, USA, 10-16 December 2023,

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elkhatib, Dr Yehia
• Authors: do Carmo Alves, M. A., Varma, A., Elkhatib, Y., and Marcolino, L. S.
• College/School: College of Science and Engineering > School of Computing Science
• Copyright Holders: Copyright © The Author(s) 2023
• First Published: First published in Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
• Publisher Policy: Reproduced with the permission of the publisher

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