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Reinforcement Learning Strategy for A-Team Solving the Resource-Constrained Project Scheduling Problem

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8083))

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Abstract

In this paper the strategy for the A-Team with Reinforcement Learning (RL) for solving the Resource Constrained Project Scheduling Problem (RCPSP) is proposed and experimentally validated. The RCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using JABAT 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 approach computational experiment has been carried out.

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Author information

Authors and Affiliations

  1. Department 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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Editor information

Editors and Affiliations

  1. Computer and Information Technology Department, University of Craiova, Bvd. Decebal 107, 200440, Craiova, Romania

    Costin Bǎdicǎ

  2. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

  3. Computer and Information Technology Department, University of Craiova, Bvd. Decebal 107, 200440, Craiova, Romania

    Marius Brezovan

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Jędrzejowicz, P., Ratajczak-Ropel, E. (2013). Reinforcement Learning Strategy for A-Team Solving the Resource-Constrained Project Scheduling Problem. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_46

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  • DOI: https://doi.org/10.1007/978-3-642-40495-5_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40494-8

  • Online ISBN: 978-3-642-40495-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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