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Social Conformity and Its Convergence for Reinforcement Learning

  • Juan A. García-Pardo
  • J. Soler
  • C. Carrascosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6251)

Abstract

A dynamic environment whose behavior may change in time presents a challenge that agents located there will have to solve. Changes in an environment e.g. a market, can be quite drastic: from changing the dependencies of some products to add new actions to build new products. The agents working in this environment would have to be ready to embrace this changes to improve their performance which otherwise would be diminished. Also, they should try to cooperate or compete against others, when appropriated, to reach their goals faster than in an individual fashion, showing an always desirable emergent behavior. In this paper a reinforcement learning method proposal, guided by social interaction between agents, is presented. The proposal aims to show that adaptation is performed independently by the society, without explicitly reporting that changes have occurred by a central authority, or even by trying to recognize those changes.

Keywords

Social Reinforcement Optimal Policy Reinforcement Learning Multiagent System Stochastic Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Sutton, R.S., Barto, A.G.: Reinforcement learning i: Introduction (1998)Google Scholar
  2. 2.
    Vidal, J.: Learning in multiagent systems: An introduction from a game-theoretic perspective. Adaptive Agents and Multi-Agent Systems, 562–562Google Scholar
  3. 3.
    Akchurina, N.: Multiagent reinforcement learning: algorithm converging to nash equilibrium in general-sum discounted stochastic games. In: AAMAS ’09: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, pp. 725–732 (2009)Google Scholar
  4. 4.
    Shoham, Y., Powers, R., Grenager, T.: Multi-agent reinforcement learning: a critical survey. In: AAAI Fall Symposium on Artificial Multi-Agent Learning, Citeseer (2004)Google Scholar
  5. 5.
    López-Paredes, A., Hernández-Iglesias, C., Gutiérrez, J.P.: Towards a new experimental socio-economics: Complex behaviour in bargaining. Journal of Socio-Economics 31(4), 423–429 (2002)CrossRefGoogle Scholar
  6. 6.
    Hu, J., Wellman, M.P.: Nash Q-learning for general-sum stochastic games. The Journal of Machine Learning Research 4, 1039–1069 (2003)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Melo, F.S., Ribeiro, M.I.: Coordinated learning in multiagent MDPs with infinite state-space. Autonomous Agents and Multi-Agent Systems, 1–47Google Scholar
  8. 8.
    Ghosh, D., Sharman, R., Raghav Rao, H., Upadhyaya, S.: Self-healing systems–survey and synthesis. Decision Support Systems 42(4), 2164–2185 (2007)CrossRefGoogle Scholar
  9. 9.
    Hu, J., Wellman, M.P.: Multiagent reinforcement learning: Theoretical framework and an algorithm (1998)Google Scholar
  10. 10.
    Tsitsiklis, J.N., Van Roy, B.: Feature-based methods for large scale dynamic programming. Machine Learning, 59–94 (1994)Google Scholar
  11. 11.
    Gordon, G.J.: Stable function approximation in dynamic programming (1995)Google Scholar
  12. 12.
    Singh, S.P., Jaakkola, T., Jordan, M.I.: Reinforcement learning with soft state aggregation. In: Advances in Neural Information Processing Systems, vol. 7, pp. 361–368. MIT Press, Cambridge (1995)Google Scholar
  13. 13.
    Tateyama, T., Kawata, S., Shimomura, Y.: A Reinforcement Learning Algorithm for Continuous State Spaces using Multiple Fuzzy-ART Networks. In: International Joint Conference on SICE-ICASE, pp. 2445–2450 (2006)Google Scholar
  14. 14.
    Helleboogh, A., Vizzari, G., Uhrmacher, A., Michel, F.: Modeling dynamic environments in multi-agent simulation. Auton. Agents Multi-Agent Syst. 14(1), 87–116 (2007)CrossRefGoogle Scholar
  15. 15.
    Dignum, V., Dignum, F., Sonenberg, L.: Towards dynamic reorganization of agent societies. In: Proceedings of Workshop on Coordination in Emergent Agent Societies, pp. 22–27 (2004)Google Scholar
  16. 16.
    Hu, J., Wellman, M.P.: Multiagent reinforcement learning in stochastic games (1999), citeseer.ist.psu.edu/hu99multiagent.html
  17. 17.
    Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 746–752. AAAI Press, Menlo Park (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan A. García-Pardo
    • 1
  • J. Soler
    • 1
  • C. Carrascosa
    • 1
  1. 1.Universidad Politécnica de ValenciaValenciaSpain

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