Monte Carlo Information-Oriented Planning

• URL: http://arxiv.org/abs/2103.11345v1
• Date: Sun, 21 Mar 2021 09:09:27 GMT
• Title: Monte Carlo Information-Oriented Planning
• Authors: Vincent Thomas, G\'er\'emy Hutin, Olivier Buffet
• Abstract summary: We discuss how to solve information-gathering problems expressed as rho-POMDPs. We build on the POMCP algorithm to propose a Monte Carlo Tree Search for rho-POMDPs.
• Score: 6.0158981171030685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we discuss how to solve information-gathering problems expressed as rho-POMDPs, an extension of Partially Observable Markov Decision Processes (POMDPs) whose reward rho depends on the belief state. Point-based approaches used for solving POMDPs have been extended to solving rho-POMDPs as belief MDPs when its reward rho is convex in B or when it is Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient on-line planner which can be used for any rho function. Adaptations are required due to the belief-dependent rewards to (i) propagate more than one state at a time, and (ii) prevent biases in value estimates. An asymptotic convergence proof to epsilon-optimal values is given when rho is continuous. Experiments are conducted to analyze the algorithms at hand and show that they outperform myopic approaches.

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