Leveraging the Value of Information in POMDP Planning

2026-04-01

AI Summary

This work addresses the severe limitations imposed by the curse of dimensionality and the curse of history in partially observable Markov decision processes (POMDPs), which hinder policy performance under finite planning horizons. The paper introduces VOIMCP, an algorithm that, for the first time, dynamically integrates the value of information (VOI) into POMDP planning. Built upon a Monte Carlo tree search framework, VOIMCP evaluates the VOI of observations in belief space and selectively prunes low-value observation branches. This approach substantially improves computational efficiency while providing theoretical guarantees of near-optimality and non-asymptotic convergence bounds. Empirical results demonstrate that VOIMCP significantly outperforms existing baselines across multiple POMDP benchmarks, confirming its superior performance and efficiency under limited computational resources.

Abstract

Partially observable Markov decision processes (POMDPs) offer a principled formalism for planning under state and transition uncertainty. Despite advances made towards solving large POMDPs, obtaining performant policies under limited planning time remains a major challenge due to the curse of dimensionality and the curse of history. For many POMDP problems, the value of information (VOI) - the expected performance gain from reasoning about observations - varies over the belief space. We introduce a dynamic programming framework that exploits this structure by conditionally processing observations based on the value of information at each belief. Building on this framework, we propose Value of Information Monte Carlo planning (VOIMCP), a Monte Carlo Tree Search algorithm that allocates computational effort more efficiently by selectively disregarding observation information when the VOI is low, avoiding unnecessary branching of observations. We provide theoretical guarantees on the near-optimality of our VOI reasoning framework and derive non-asymptotic convergence bounds for VOIMCP. Simulation evaluations demonstrate that VOIMCP outperforms baselines on several POMDP benchmarks.

Problem

• POMDP
• value of information
• planning under uncertainty
• computational efficiency
• belief space

Innovation

• Value of Information
• POMDP
• Monte Carlo Tree Search
• Dynamic Programming
• Non-asymptotic Convergence