Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots.

ABSTRACT: We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions. Then, we present an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRF. Our approach acquires information from the agent's action outcomes to adapt online the MRF if a mismatch is detected between the MRF and the true state. We test this technique on two domains, r

SUBMITTER: Zuccotto M 

PROVIDER: S-EPMC9343685 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature