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International Workshop on Intelligent Virtual Agents

IVA 2003: Intelligent Virtual Agents pp 361–362Cite as

Improving Reinforcement Learning Algorithm Using Emotions in a Multi-agent System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2792))

Abstract

A new approach for learning is presented here. The system that is named Sepanta consists of a set of agents that are doing a task. Behaviors of agents are adapted according to emotional signals provided by two parts called emotional critic: one global, generating signal for all agents and, one local, for each agent generating signal specifically for it. The main learning algorithm is Q-Learning that is improved by using these signals. Simulation is done for the task of pushing a mass by a number of robots. The main idea for this work has been a learning method that is tuned by emotion signals supplied by critics for assessing the present situation.

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References

  1. Lucas, C., Jazbi, S.A., Fatourechi, M., Farshad, M.: Cognitive action selection with neurocontrollers. In: Third Iran-Armenia Workshop on Neural Networks, Yerevan, Armenia (2000)

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  2. Maes, P.: Modeling adaptive autonomous agents. In: Langton, C.G. (ed.) Artificial Life: An Overview, pp. 135–162. The MIT Press, Cambridge (1995)

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  3. Sutton, R.S., Barto, A.G.: Reinforcement Learning: an introduction. MIT Press, Cambridge

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Author information

Authors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Tehran, Tehran, Iran

    Roozbeh Daneshvar

  2. Center of Excellence for Control and Intelligent Processing, Department of Electrical and Computer Engineering, University of Tehran. and School of Intelligent Systems, IPM,  

    Caro Lucas

Authors
  1. Roozbeh Daneshvar
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  2. Caro Lucas
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Editor information

Editors and Affiliations

  1. University of Applied Sciences Augsburg, Friedberger Str. 2a, 86161, Augsburg, Germany

    Thomas Rist

  2. School of Mathematical and Computer Sciences, Heriot Watt University, EH14 4AS, Edinburgh, Scotland

    Ruth S. Aylett

  3. BT plc, Adastral Park, IP5 3RE, Ipswich, UK

    Daniel Ballin

  4. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, 90292-6695, Marina del Rey, CA

    Jeff Rickel

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© 2003 Springer-Verlag Berlin Heidelberg

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Cite this paper

Daneshvar, R., Lucas, C. (2003). Improving Reinforcement Learning Algorithm Using Emotions in a Multi-agent System. In: Rist, T., Aylett, R.S., Ballin, D., Rickel, J. (eds) Intelligent Virtual Agents. IVA 2003. Lecture Notes in Computer Science(), vol 2792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39396-2_64

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  • DOI: https://doi.org/10.1007/978-3-540-39396-2_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20003-1

  • Online ISBN: 978-3-540-39396-2

  • eBook Packages: Springer Book Archive

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