Abstract
One of the most formidable issues of RL application to real robot tasks is how to find a suitable state space, and this has been much more serious since recent robots tends to have more sensors and the environment including other robots becomes more complicated. In order to cope with the issue, this paper presents a method of self task decomposition for modular learning system based on self-interpretation of instructions given by a coach. The proposed method is applied to a simple soccer situation in the context of RoboCup.
Chapter PDF
Similar content being viewed by others
References
Connell, J.H., Mahadevan, S.: Robot Learning. Kluwer Academic Publishers, Dordrecht (1993)
Asada, M., Noda, S., Tawaratumida, S., Hosoda, K.: Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Machine Learning 23, 279–303 (1996)
Stone, P., Veloso, M.: Layered approach to learning client behaviors in the robocup soccer server. Applied Artificial Intelligence 12(2-3) (1998)
Stone, P., Veloso, M.M.: Team-partitioned, opaque-transition reinforcement learning. In: Asada, M., Kitano, H. (eds.) RoboCup 1998. LNCS, vol. 1604, pp. 261–272. Springer, Heidelberg (1999)
Digney, B.L.: Emergent hierarchical control structures: Learning reactive/hierarchical relationships in reinforcement environments. In: Maes, P., Mataric, M.J., Meyer, J.-A., Pollack, J., Wilson, S.W. (eds.) From animals to animats 4: Proceedings of The Fourth Conference on the Simulation of Adaptive Behavior: SAB 1996, pp. 363–372. MIT Press, Cambridge (1996)
Digney, B.L.: Learning hierarchical control structures for multiple tasks and changing environments. In: Pfeifer, R., Blumberg, B., Meyer, J.-A., Wilson, S.W. (eds.) From animals to animats 5: Proceedings of The Fifth Conference on the Simulation of Adaptive Behavior: SAB 1998, pp. 321–330. MIT Press, Cambridge (1998)
Hengst, B.: Generating hierarchical structure in reinforcement learning from state variables. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886. Springer, Heidelberg (2000)
Hengst, B.: Discovering hierarchy in reinforcement learning with HEXQ. In: Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pp. 243–250 (2002)
Whitehead, S.D.: Complexity and cooperation in q-learning. In: Proceedings Eighth International Workshop on Machine Learning (ML 1991), pp. 363–367 (1991)
Asada, M., Noda, S., Tawaratsumida, S., Hosoda, K.: Vision-based reinforcement learning for purposive behavior acquisition. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 146–153 (1995)
Takahashi, Y., Hikita, K., Asada, M.: Incremental purposive behavior acquisition based on self-interpretation of instructions by coach. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 686–693 (October 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takahashi, Y., Nishi, T., Asada, M. (2006). Self Task Decomposition for Modular Learning System Through Interpretation of Instruction by Coach. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds) RoboCup 2005: Robot Soccer World Cup IX. RoboCup 2005. Lecture Notes in Computer Science(), vol 4020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780519_64
Download citation
DOI: https://doi.org/10.1007/11780519_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35437-6
Online ISBN: 978-3-540-35438-3
eBook Packages: Computer ScienceComputer Science (R0)