D 2018

Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-Sum Objectives

CHATTERJEE, Krishnendu, Adrián ELGYUTT, Petr NOVOTNÝ a Owen ROUILLÉ

Základní údaje

Originální název

Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-Sum Objectives

Autoři

CHATTERJEE, Krishnendu, Adrián ELGYUTT, Petr NOVOTNÝ a Owen ROUILLÉ

Vydání

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), od s. 4692--4699, 7 s. 2018

Nakladatel

ijcai.org

Další údaje

Typ výsledku

Stať ve sborníku

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

ISBN

978-0-9992411-2-7

DOI

Klíčová slova anglicky

POMDPs; Planning under Uncertainty; Planning with Incomplete Information

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 26. 9. 2019 10:15, doc. RNDr. Petr Novotný, Ph.D.

Anotace

V originále

Partially-observable Markov decision processes (POMDPs) with discounted-sum payoff are a standard framework to model a wide range of problems related to decision making under uncertainty. Traditionally, the goal has been to obtain policies that optimize the expectation of the discounted-sum payoff. A key drawback of the expectation measure is that even low probability events with extreme payoff can significantly affect the expectation, and thus the obtained policies are not necessarily risk averse. An alternate approach is to optimize the probability that the payoff is above a certain threshold, which allows to obtain risk-averse policies, but ignore optimization of the expectation. We consider the expectation optimization with probabilistic guarantee (EOPG) problem where the goal is to optimize the expectation ensuring that the payoff is above a given threshold with at least a specified probability. We present several results on the EOPG problem, including the first algorithm to solve it.