Optimizing Expectation with Guarantees in POMDPs

CHATTERJEE, Krishnendu, Petr NOVOTNÝ, Guillermo A. PÉREZ, Jean-Francois RASKIN and Djordje ŽIKELIĆ. Optimizing Expectation with Guarantees in POMDPs. Online. In Satinder P. Singh and Shaul Markovitch. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI). AAAI Press, 2017, p. 3725--3732.

Basic information

• Original name: Optimizing Expectation with Guarantees in POMDPs
• Authors: CHATTERJEE, Krishnendu, Petr NOVOTNÝ, Guillermo A. PÉREZ, Jean-Francois RASKIN and Djordje ŽIKELIĆ.
• Edition: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), p. 3725--3732, 2017.
• Publisher: AAAI Press

Other information

• Type of outcome: Proceedings paper
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• Keywords in English: Partially-observable Markov decision processes; Discounted payoff; Probabilistic planning; Verification
• Tags: International impact, Reviewed

Abstract

A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy σ such that a) each possible outcome of σ yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of σ is optimal (or near-optimal) among all policies satisfying a). We present a practical approach to tackle the GPO problem and evaluate it on standard POMDP benchmarks.