Multi-Objective POMDPs with Lexicographic Reward Preferences

We propose a model, Lexicographic Partially Observable Markov Decision Process (LPOMDP), which extends POMDPs with lexicographic preferences over multiple value functions. It allows for slack--slightly less-than-optimal values--for higher-priority preferences to facilitate improvement in lower-priority value functions. Many real life situations are naturally captured by LPOMDPs with slack. We consider a semi-autonomous driving scenario in which time spent on the road is minimized, while maximizing time spent driving autonomously. We propose two solutions to LPOMDPs--Lexicographic Value Iteration (LVI) and Lexicographic Point-Based Value Iteration (LPBVI), establishing convergence results and correctness within strong slack bounds. We test the algorithms using real-world road data provided by Open Street Map (OSM) within 10 major cities. Finally, we present GPU-based optimizations for point-based solvers, demonstrating that their application enables us to quickly solve vastly larger LPOMDPs and other variations of POMDPs.