Pareto-DQN: Approximating the Pareto front in complex multi-objective decision problems

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch

Abstract

In many real-world problems, one needs to care about multiple objectives. These objectives can be contradicting and, depending on the decision maker, the different compromises will be ranked differently. In this preliminary work, we propose a novel algorithm: Pareto-DQN, that will estimate the Pareto front of complex environment, with a high-dimensional state-space. As a proof-of-concept, we successfully apply our algorithm to the Deep-Sea-Treasure environment, a well known Multi-objective reinforcement learning benchmark.
Original languageEnglish
Title of host publicationProceedings of the Adaptive and Learning Agents Workshop 2019 (ALA-19) at AAMAS
Number of pages6
Publication statusPublished - 13 May 2019
Event2019 Adaptive Learning Agents (ALA) workshop: Workshop of the AAMAS conference - Montreal, Canada
Duration: 13 May 201914 May 2019
https://ala2019.vub.ac.be

Workshop

Workshop2019 Adaptive Learning Agents (ALA) workshop: Workshop of the AAMAS conference
Abbreviated titleALA 2019
CountryCanada
CityMontreal
Period13/05/1914/05/19
Internet address

Fingerprint Dive into the research topics of 'Pareto-DQN: Approximating the Pareto front in complex multi-objective decision problems'. Together they form a unique fingerprint.

Cite this