A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track

Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; Patrick Mannion; Ann Nowe; Gabriel De Oliveira Ramos; Marcello Restelli; Peter Vamplew; Diederik M. Roijers

A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track

Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowe, Gabriel De Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

2 Citations (Scopus)

Abstract

Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple -- often conflicting -- objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results.
This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper serves as a guide for the application of explicitly multi-objective methods to difficult problems.

Original language	English
Title of host publication	The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)
Publisher	International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages	1988-1990
Number of pages <span style="color:red"p> <font size="1.5"> ✽ </span> </font>	3
Volume	2023-May
ISBN (Electronic)	978-1-4503-9432-1
Publication status	Published - May 2023
Event	The 22nd International Conference on Autonomous Agents and Multiagent Systems - London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 https://aamas2023.soton.ac.uk

Publication series

Name	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN (Print)	1548-8403

Conference

Conference	The 22nd International Conference on Autonomous Agents and Multiagent Systems
Abbreviated title	AAMAS 2023
Country/Territory	United Kingdom
City	London
Period	29/05/23 → 2/06/23
Internet address	https://aamas2023.soton.ac.uk

Bibliographical note

Funding Information:
Conor F. Hayes is funded by the University of Galway Hardiman Scholarship. This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program. Roxana Rădulescu is supported by the Research Foundation Flanders (FWO postdoctoral fellowship 1286223N). Johan Källström and Fredrik Heintz were partially supported by the Swedish Governmental Agency for Innovation Systems (grant NFFP7/2017-04885), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Luisa Zintgraf was supported by the 2017 Microsoft Research PhD Scholarship Program, and the 2020 Microsoft Research EMEA PhD Award.

Publisher Copyright:
© 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Access to Document

https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p1988.pdf

Cite this

Hayes, C. F., Radulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L., Dazeley, R., Heintz, F., Howley, E., Irissappane, A. A., Mannion, P., Nowe, A., De Oliveira Ramos, G., Restelli, M., Vamplew, P., & Roijers, D. M. (2023). A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track. In The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) (Vol. 2023-May, pp. 1988-1990). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p1988.pdf

Hayes, Conor F. ; Radulescu, Roxana ; Bargiacchi, Eugenio ; Källström, Johan ; Macfarlane, Matthew ; Reymond, Mathieu ; Verstraeten, Timothy ; Zintgraf, Luisa ; Dazeley, Richard ; Heintz, Fredrik ; Howley, Enda ; Irissappane, Athirai A. ; Mannion, Patrick ; Nowe, Ann ; De Oliveira Ramos, Gabriel ; Restelli, Marcello ; Vamplew, Peter ; Roijers, Diederik M. / A Brief Guide to Multi-Objective Reinforcement Learning and Planning : JAAMAS Track. The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). Vol. 2023-May International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023. pp. 1988-1990 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

@inproceedings{6f906192628547bd894428f171bf01ce,

title = "A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track",

abstract = "Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple -- often conflicting -- objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper serves as a guide for the application of explicitly multi-objective methods to difficult problems.",

author = "Hayes, {Conor F.} and Roxana Radulescu and Eugenio Bargiacchi and Johan K{\"a}llstr{\"o}m and Matthew Macfarlane and Mathieu Reymond and Timothy Verstraeten and Luisa Zintgraf and Richard Dazeley and Fredrik Heintz and Enda Howley and Irissappane, {Athirai A.} and Patrick Mannion and Ann Nowe and {De Oliveira Ramos}, Gabriel and Marcello Restelli and Peter Vamplew and Roijers, {Diederik M.}",

note = "Funding Information: Conor F. Hayes is funded by the University of Galway Hardiman Scholarship. This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artifici{\"e}le Intelligentie (AI) Vlaanderen” program. Roxana R{\u a}dulescu is supported by the Research Foundation Flanders (FWO postdoctoral fellowship 1286223N). Johan K{\"a}llstr{\"o}m and Fredrik Heintz were partially supported by the Swedish Governmental Agency for Innovation Systems (grant NFFP7/2017-04885), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Luisa Zintgraf was supported by the 2017 Microsoft Research PhD Scholarship Program, and the 2020 Microsoft Research EMEA PhD Award. Publisher Copyright: {\textcopyright} 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; The 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 ; Conference date: 29-05-2023 Through 02-06-2023",

year = "2023",

month = may,

language = "English",

volume = "2023-May",

series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",

publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",

pages = "1988--1990",

booktitle = "The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)",

url = "https://aamas2023.soton.ac.uk",

}

Hayes, CF, Radulescu, R , Bargiacchi, E, Källström, J, Macfarlane, M, Reymond, M , Verstraeten, T, Zintgraf, L, Dazeley, R, Heintz, F, Howley, E, Irissappane, AA, Mannion, P, Nowe, A, De Oliveira Ramos, G, Restelli, M, Vamplew, P & Roijers, DM 2023, A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track. in The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). vol. 2023-May, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 1988-1990, The 22nd International Conference on Autonomous Agents and Multiagent Systems, London, United Kingdom, 29/05/23. <https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p1988.pdf>

A Brief Guide to Multi-Objective Reinforcement Learning and Planning : JAAMAS Track. / Hayes, Conor F.; Radulescu, Roxana ; Bargiacchi, Eugenio; Källström, Johan; Macfarlane, Matthew; Reymond, Mathieu ; Verstraeten, Timothy; Zintgraf, Luisa; Dazeley, Richard; Heintz, Fredrik; Howley, Enda; Irissappane, Athirai A.; Mannion, Patrick; Nowe, Ann; De Oliveira Ramos, Gabriel; Restelli, Marcello; Vamplew, Peter; Roijers, Diederik M.

The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). Vol. 2023-May International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023. p. 1988-1990 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

TY - GEN

T1 - A Brief Guide to Multi-Objective Reinforcement Learning and Planning

T2 - The 22nd International Conference on Autonomous Agents and Multiagent Systems

AU - Hayes, Conor F.

AU - Radulescu, Roxana

AU - Bargiacchi, Eugenio

AU - Källström, Johan

AU - Macfarlane, Matthew

AU - Reymond, Mathieu

AU - Verstraeten, Timothy

AU - Zintgraf, Luisa

AU - Dazeley, Richard

AU - Heintz, Fredrik

AU - Howley, Enda

AU - Irissappane, Athirai A.

AU - Mannion, Patrick

AU - Nowe, Ann

AU - De Oliveira Ramos, Gabriel

AU - Restelli, Marcello

AU - Vamplew, Peter

AU - Roijers, Diederik M.

N1 - Funding Information: Conor F. Hayes is funded by the University of Galway Hardiman Scholarship. This research was supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program. Roxana Rădulescu is supported by the Research Foundation Flanders (FWO postdoctoral fellowship 1286223N). Johan Källström and Fredrik Heintz were partially supported by the Swedish Governmental Agency for Innovation Systems (grant NFFP7/2017-04885), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Luisa Zintgraf was supported by the 2017 Microsoft Research PhD Scholarship Program, and the 2020 Microsoft Research EMEA PhD Award. Publisher Copyright: © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

PY - 2023/5

Y1 - 2023/5

N2 - Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple -- often conflicting -- objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper serves as a guide for the application of explicitly multi-objective methods to difficult problems.

AB - Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple -- often conflicting -- objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper serves as a guide for the application of explicitly multi-objective methods to difficult problems.

UR - http://www.scopus.com/inward/record.url?scp=85171271956&partnerID=8YFLogxK

M3 - Conference paper

VL - 2023-May

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 1988

EP - 1990

BT - The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Y2 - 29 May 2023 through 2 June 2023

ER -

Hayes CF, Radulescu R , Bargiacchi E, Källström J, Macfarlane M, Reymond M et al. A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track. In The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). Vol. 2023-May. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). 2023. p. 1988-1990. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

A Brief Guide to Multi-Objective Reinforcement Learning and Planning: JAAMAS Track

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

VLAAI1: Flanders Artificial Intelligence Research program (FAIR) – second cycle

FWOTM1108: Decision-making in team-reward multi-objective multi-agent domains