A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains

TL;DR

This paper introduces a novel approximation scheme for POMDPs that uses an oracle to provide regional state information, simplifying the problem and balancing computational efficiency with solution accuracy.

Contribution

It proposes a new region-observable POMDP model and an efficient exact algorithm, enhancing planning in partially observable stochastic domains.

Findings

The approximation scheme effectively balances computational time and solution quality.

The new exact algorithm outperforms previous methods in efficiency.

Region observability simplifies solving complex POMDPs.

Abstract

Partially observable Markov decision processes (POMDPs) are a natural model for planning problems where effects of actions are nondeterministic and the state of the world is not completely observable. It is difficult to solve POMDPs exactly. This paper proposes a new approximation scheme. The basic idea is to transform a POMDP into another one where additional information is provided by an oracle. The oracle informs the planning agent that the current state of the world is in a certain region. The transformed POMDP is consequently said to be region observable. It is easier to solve than the original POMDP. We propose to solve the transformed POMDP and use its optimal policy to construct an approximate policy for the original POMDP. By controlling the amount of additional information that the oracle provides, it is possible to find a proper tradeoff between computational time and approximation quality. In terms of algorithmic contributions, we study in details how to exploit region observability in solving the transformed POMDP. To facilitate the study, we also propose a new exact algorithm for general POMDPs. The algorithm is conceptually simple and yet is significantly more efficient than all previous exact algorithms.