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International Conference on Intelligent Decision Technologies

IDT 2017: Intelligent Decision Technologies pp 537–548Cite as

Reinforcement Learning Strategy for Solving the MRCPSP by a Team of Agents

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 39))

Abstract

In this paper the strategy for the A-Team with Reinforcement Learning (RL) approach for solving the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) is proposed and experimentally validated. The MRCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by the static strategy. In this paper the dynamic learning strategy is suggested. The proposed strategy based on reinforcement learning supervises interactions between optimization agents and the common memory. To validate the proposed approach computational experiment has been carried out.

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Notes

  1. 1.

    See PSPLIB at http://www.om-db.wi.tum.de/psplib/.

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Authors and Affiliations

  1. Chair of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz & Ewa Ratajczak-Ropel

Authors
  1. Piotr Jędrzejowicz
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  2. Ewa Ratajczak-Ropel
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Corresponding author

Correspondence to Ewa Ratajczak-Ropel .

Editor information

Editors and Affiliations

  1. FCT, Universidade Nova de Lisboa, Caparica, Portugal

    Rui Neves-Silva

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Jędrzejowicz, P., Ratajczak-Ropel, E. (2015). Reinforcement Learning Strategy for Solving the MRCPSP by a Team of Agents. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_46

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

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