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

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

Great Hall & Hall B1+B2 (level 1) #1427


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.

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