Abstract
In this paper, we discuss guidelines for a reward design problem that defines when and what amount of reward should be given to the agents, within the context of reinforcement learning approach. We take keepaway soccer as a standard task of multiagent domain which requires skilled teamwork. The difficulties of designing reward for good teamwork are due to its features as follows: i) since it is a continuing task which has no explicit goal, it is hard to tell when reward should be given to the agents, ii) since it is a multiagent cooperative task, it is hard to make a fair share of the reward for each agent’s contribution. Through some experiments, we show that reward design have a major effect on the agent’s behavior, and introduce the reward function that makes agents perform keepaway successfully.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Stone, P., Sutton, R.S.: Keepaway soccer: a machine learning testbed. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 214–223. Springer, Heidelberg (2002)
Kuhlmann, G., Stone, P.: Progress in learning 3 vs. 2 keepaway. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, Springer, Heidelberg (2004)
Stone, P., Sutton, R.S., Kuhlmann, G.: Reinforcement learning for robocup soccer keepaway. Adaptive Behavior 13(3), 165–188 (2005)
Stone, P., Kuhlmann, G., Taylor, M.E., Liu, Y.: Keepaway soccer: From machine learning testbed to benchmark. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, Springer, Heidelberg (to appear, 2006)
Ng, A.Y., Russell, S.: Algorithms for inverse reinforcement learning. In: Proc. 17th International Conf. on Machine Learning, pp. 663–670. Morgan Kaufmann, San Francisco (2000)
Singh, S.P., Sutton, R.S.: Reinforcement learning with replacing eligibility traces. Machine Learning 22(1-3), 123–158 (1996)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, The MIT Press (1998)
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
Tanaka, N., Arai, S. (2006). Teamwork Formation for Keepaway in Robotics Soccer (Reinforcement Learning Approach). In: Shi, ZZ., Sadananda, R. (eds) Agent Computing and Multi-Agent Systems. PRIMA 2006. Lecture Notes in Computer Science(), vol 4088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802372_28
Download citation
DOI: https://doi.org/10.1007/11802372_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36707-9
Online ISBN: 978-3-540-36860-1
eBook Packages: Computer ScienceComputer Science (R0)