Online POMDP Planning via Simplification

TL;DR

This paper introduces SITH-BSP, an algorithm for online POMDP planning that simplifies belief representations to achieve faster computation without losing optimality, validated through simulation with sampling-based bounds.

Contribution

The paper presents a novel belief simplification method with bounds for POMDPs, enabling guaranteed optimal solutions with significant speedup in online planning.

Findings

Significant computational speedup demonstrated in simulations.

Belief simplification with bounds maintains optimality.

Novel bounds for differential entropy with sampling-based beliefs.

Abstract

In this paper, we consider online planning in partially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. Our key contribution is a novel algorithmic approach, Simplified Information Theoretic Belief Space Planning (SITH-BSP), which aims to speed-up POMDP planning considering belief-dependent rewards, without compromising on the solution's accuracy. We do so by mathematically relating the simplified elements of the problem to the corresponding counterparts of the original problem. Specifically, we focus on belief simplification and use it to formulate bounds on the corresponding original belief-dependent rewards. These bounds in turn are used to perform branch pruning over the belief tree, in the process of calculating the optimal policy. We further introduce the notion of adaptive simplification, while re-using calculations between different simplification levels and exploit it to prune, at each level in the belief tree, all branches but one. Therefore, our approach is guaranteed to find the optimal solution of the original problem but with substantial speedup. As a second key contribution, we derive novel analytical bounds for differential entropy, considering a sampling-based belief representation, which we believe are of interest on their own. We validate our approach in simulation using these bounds and where simplification corresponds to reducing the number of samples, exhibiting a significant computational speedup while yielding the optimal solution.