Model-Based Reinforcement Learning in a Complex Domain

  • Shivaram Kalyanakrishnan
  • Peter Stone
  • Yaxin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5001)

Abstract

Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the environment. Model-free algorithms perform updates solely bas ed on observed experiences. By contrast, model-based algorithms learn a model of the environment that effectively simulates its dynamics. The model may be used to simulate experiences or to plan into the future, potentially expediting the learning process. This paper presents a model-based reinforcement learning approach for Keepaway, a complex, continuous, stochastic, multiagent subtask of RoboCup simulated soccer. First, we propose the design of an environmental model that is partly learned based on the agent’s experiences. This model is then coupled with the reinforcement learning algorithm to learn an action selection policy. We evaluate our method through empirical comparisons with model-free approaches that have been previously applied successfully to this task. Results demonstrate significant gains in the learning speed and asymptotic performance of our method. We also show that the learned model can be used effectively as part of a planning-based approach with a hand-coded policy.

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References

  1. 1.
    Albus, J.S.: Brains, Behavior, and Robotics. BYTE Books, Peterborough (1981)Google Scholar
  2. 2.
    Atkeson, C., Santamaría, J.: A comparison of direct and model-based reinforcement learning. In: IEEE International Conference on Robotics and Automation, vol. 4, pp. 3557–3564 (April 1997)Google Scholar
  3. 3.
    Boone, G.: Efficient reinforcement learning: model-based acrobot control. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 229–234 (April 1997)Google Scholar
  4. 4.
    Bradtke, S.J., Duff, M.O.: Reinforcement learning methods for continuous-time Markov decision problems. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 393–400. The MIT Press (1995)Google Scholar
  5. 5.
    Kalyanakrishnan, S., Liu, Y., Stone, P.: Half field offense in RoboCup soccer: A multiagent reinforcement learning case study. In: Proceedings of the RoboCup International Symposium 2006 (June 2006)Google Scholar
  6. 6.
    Kalyanakrishnan, S., Stone, P.: Batch reinforcement learning in a complex domain. In: The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (May 2007)Google Scholar
  7. 7.
    Lin, L.-J.: Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning 8, 293–321 (1992)Google Scholar
  8. 8.
    Chen, M., Foroughi, E., Heintz, F., Huang, Z., Kapetanakis, S., Kostiadis, K., Kummeneje, J., Noda, I., Obst, O., Riley, P., Steffens, T., Wang, Y., Yin, X.: Users manual: RoboCup soccer server — for soccer server version 7.07 and later. In: The RoboCup Federation (August 2002)Google Scholar
  9. 9.
    Ng, A.Y., Kim, H.J., Jordan, M.I., Sastry, S.: Autonomous helicopter flight via reinforcement learning. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16, MIT Press, Cambridge (2004)Google Scholar
  10. 10.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley and Sons, New York (1994)MATHGoogle Scholar
  11. 11.
    Stone, P., Sutton, R.S., Kuhlmann, G.: Reinforcement learning for RoboCup-soccer keepaway. Adaptive Behavior 13(3), 165–188 (2005)CrossRefGoogle Scholar
  12. 12.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  13. 13.
    Sutton, R.S., Precup, D., Singh, S.P.: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112(1-2), 181–211 (1999)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Tesauro, G.: Practical issues in temporal difference learning. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (eds.) Advances in Neural Information Processing Systems, vol. 4, pp. 259–266. Morgan Kaufmann Publishers, Inc. (1992)Google Scholar
  15. 15.
    Tsitsiklis, J.N., Roy, B.V.: Feature-based methods for large scale dynamic programming. Machine Learning 22(1-3), 59–94 (1996)MATHCrossRefGoogle Scholar
  16. 16.
    Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine Learning 8(3-4), 279–292 (1992)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shivaram Kalyanakrishnan
    • 1
  • Peter Stone
    • 1
  • Yaxin Liu
    • 1
  1. 1.Department of Computer SciencesThe University of Texas at AustinAustin 

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