Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs

TL;DR

This paper presents Voronoi Progressive Widening (VPW), a novel approach for efficiently solving continuous and hybrid POMDPs through tree search algorithms, with theoretical guarantees and superior empirical performance.

Contribution

It introduces VPW as a generalization of existing methods, providing the first globally convergent algorithm for continuous POMDPs and a practical, high-performing algorithm VOMCPOW.

Findings

VOWSS provides theoretical guarantees for VPW-based solvers.

VOMCPOW outperforms state-of-the-art algorithms in simulations.

First algorithm with global convergence for continuous POMDPs.

Abstract

This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs). Tree search algorithms can use VPW to effectively handle continuous or hybrid action spaces by efficiently balancing local and global action searching. This paper proposes two VPW-based algorithms and analyzes them from theoretical and simulation perspectives. Voronoi Optimistic Weighted Sparse Sampling (VOWSS) is a theoretical tool that justifies VPW-based online solvers, and it is the first algorithm with global convergence guarantees for continuous state, action, and observation POMDPs. Voronoi Optimistic Monte Carlo Planning with Observation Weighting (VOMCPOW) is a versatile and efficient algorithm that consistently outperforms state-of-the-art POMDP algorithms in several simulation experiments.