Addressing Myopic Constrained POMDP Planning with Recursive Dual Ascent

Published: 12 Feb 2024, Last Modified: 06 Mar 2024ICAPS 2024EveryoneRevisionsBibTeXCC BY 4.0
Keywords: CPOMDP, Constrained Planning, Lagrangian, Online Planning, Uncertainty, Heuristic Search
Abstract: Lagrangian-guided Monte Carlo tree search with global dual ascent has been applied to solve large constrained partially observable Markov decision processes (CPOMDPs) online. In this work, we demonstrate that these global dual parameters can lead to myopic action selection during exploration, ultimately leading to suboptimal decision making. To address this, we introduce history-dependent dual variables that guide local action selection and are optimized with recursive dual ascent. We empirically compare the performance of our approach on a motivating toy example and two large CPOMDPs, demonstrating improved exploration, and ultimately, safer outcomes.
Category: Short
Student: Graduate
Supplemtary Material: pdf
Submission Number: 173