Advertisement

Skill Combination for Reinforcement Learning

  • Zhihui Luo
  • David Bell
  • Barry McCollum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

Recently researchers have introduced methods to develop reusable knowledge in reinforcement learning (RL). In this paper, we define simple principles to combine skills in reinforcement learning. We present a skill combination method that uses trained skills to solve different tasks in a RL domain. Through this combination method, composite skills can be used to express tasks at a high level and they can also be re-used with different tasks in the context of the same problem domains. The method generates an abstract task representation based upon normal reinforcement learning which decreases the information coupling of states thus improving an agent’s learning. The experimental results demonstrate that the skills combination method can effectively reduce the learning space, and so accelerate the learning speed of the RL agent. We also show in the examples that different tasks can be solved by combining simple reusable skills.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Konidaris, G.D., Barto, A.G.: Building Portable Options: Skill Transfer in Reinforcement Learning. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence 2007, Hyderabad, India, January 6-12, 2007 (2007)Google Scholar
  2. 2.
    Taylor, M.E., Stone, P.: Cross-Domain Transfer for Reinforcement Learning. In: ICML 2007. Proceedings of the Twenty-Fourth International Conference on Machine Learning (2007)Google Scholar
  3. 3.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley-Interscience, Chichester (2005)Google Scholar
  4. 4.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence, pp. 763–788 (2003)Google Scholar
  5. 5.
    Watkins, C., Dayan, P.: Q-Learning. Machine Learning 8(3-4), 279–292 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  7. 7.
    Liu, Y., Stone, P.: Stone. Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (2006)Google Scholar
  8. 8.
    Taylor, M.E., Whiteson, S., Stone, a.P.: Transfer via InterTask Mappings in Policy Search Reinforcement Learning. In: The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007 (2007)Google Scholar
  9. 9.
    Konidaris, G., Barto, A.: Autonomous Shaping: Knowledge Transfer in Reinforcement Learning. In: Proceedings of the Twenty Third International Conference on Machine Learning, Pittsburgh (2006)Google Scholar
  10. 10.
    Kalyanakrishnan, S., Stone, P., Liu, Y.: Model-based Reinforcement Learning in a Complex Domain. In: RoboCup-2007: Robot Soccer World Cup XI, Springer, Berlin (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhihui Luo
    • 1
  • David Bell
    • 1
  • Barry McCollum
    • 1
  1. 1.School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast 

Personalised recommendations