Skip to main content
SpringerLink
Log in

Asynchronous Vector Iteration in Multi-objective Markov Decision Processes

  • Conference paper
  • First Online:
Advances in Artificial Intelligence (CAEPIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12882))

Included in the following conference series:

  • 1167 Accesses

Abstract

This paper presents new algorithms to solve Multi-Objective Markov Decision Processes (MOMDPs). Namely, we present Multi-objective Dynamic Programming variants of Value Iteration such that the values for every state are updated in some heuristic order. The performance of these algorithms is evaluated applying them to benchmark problems with two and three objectives.

Supported by: Plan Propio de Investigación de la Universidad de Málaga - Campus de Excelencia Internacional Andalucía Tech. L. Mandow is supported by project Rhea P18-FR-1081 funded by Junta de Andalucía (co-financed by FEDER funds), Spain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barrett, L., Narayanan, S.: Learning all optimal policies with multiple criteria. In: Proceedings of the 25th International Conference on Machine Learning, pp. 41–47 (2008)

    Google Scholar 

  2. Dai, P., Hansen, E.A.: Prioritizing Bellman backups without a priority queue. In: Proceedings of the Seventeenth International Conference on International Conference on Automated Planning and Scheduling, pp. 113–119. AAAI Press (2007)

    Google Scholar 

  3. Drugan, M., Wiering, M., Vamplew, P., Chetty, M.: Special issue on multi-objective reinforcement learning. Neurocomputing 263, 1–2 (2017)

    Article  Google Scholar 

  4. Mandow, L., Pérez de la Cruz, J.L., Pozas, N.: Multi-objective dynamic programming with limited precision. arXiv:2009.08198 (2020)

  5. Mausam, A.K.: Planning with Markov decision processes. An AI perspective. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool (2012)

    Google Scholar 

  6. Roijers, D.M., Whiteson, S.: Multi-objective decision making. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool (2017)

    Google Scholar 

  7. Vamplew, P., Dazeley, R., Berry, A., Issabekov, R., Dekker, E.: Empirical evaluation methods for multiobjective reinforcement learning algorithms. Mach. Learn. 84, 51–80 (2011)

    Article  MathSciNet  Google Scholar 

  8. White, D.J.: Multi-objective infinite-horizon discounted Markov decision processes. J. Math. Anal. Appl. 89, 639–647 (1982)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Univ. de Málaga, Andalucía Tech, Dpt. Lenguajes y Ciencias Comp., Málaga, Spain

    Ekaterina Sedova, Lawrence Mandow & José-Luis Pérez-de-la-Cruz

Authors
  1. Ekaterina Sedova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lawrence Mandow
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José-Luis Pérez-de-la-Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José-Luis Pérez-de-la-Cruz .

Editor information

Editors and Affiliations

  1. University of Malaga, Málaga, Spain

    Enrique Alba

  2. University of Malaga, Málaga, Spain

    Gabriel Luque

  3. University of Malaga, Málaga, Spain

    Francisco Chicano

  4. University of Malaga, Málaga, Spain

    Carlos Cotta

  5. Technical University of Madrid, Madrid, Spain

    David Camacho

  6. University of Malaga, Málaga, Spain

    Manuel Ojeda-Aciego

  7. University of Oviedo, Oviedo, Spain

    Susana Montes

  8. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

  9. University of Seville, Seville, Spain

    José Riquelme

  10. University of Malaga, Málaga, Spain

    Rodrigo Gil-Merino

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sedova, E., Mandow, L., Pérez-de-la-Cruz, JL. (2021). Asynchronous Vector Iteration in Multi-objective Markov Decision Processes. In: Alba, E., et al. Advances in Artificial Intelligence. CAEPIA 2021. Lecture Notes in Computer Science(), vol 12882. Springer, Cham. https://doi.org/10.1007/978-3-030-85713-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85713-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85712-7

  • Online ISBN: 978-3-030-85713-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation