Abstract
Soccer robotics is an emerging field that combines artificial intelligence and mobile robotics with the popular sport of soccer. Robotic soccer agents need to cooperate to complete tasks or subtasks, one way is by learning to coordinate their action. Leadingpass is considered as a task that had to be performed successfully by the team, or opponent could intercept the ball that leads the team to lose the game. This paper describes how Reinforcement Learning (RL) methods are applied to the learning scenario, that the learning agents cooperatively complete the leadingpass task in the Gridworld soccer environment. Not only RL algorithms for single agent case, but also for multi agent case.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carvalho, A., Oliveira, R.: Reinforcement learning for the soccer dribbling task. In: IEEE Conference on Computational Intelligence and Games, CIG 2011. IEEE, Seoul (2011)
Withopf, D., Riedmiller, M.: Effective Methods for Reinforcement Learning in Large Multi-Agent Domains. it - Information Technology Journal 5(47), 241–249 (2005)
Stone, P., Sutton, R.S., Kuhlmann, G.: Reinforcement learning for RoboCup-soccer keepaway. Adaptive Behavior, 13 (2005)
Celiberto, L.A., et al.: Reinforcement Learning with Case-Based Heuristics for RoboCup Soccer Keepaway. In: Robotics Symposium and Latin American Robotics Symposium (SBR-LARS), Brazilian (2012)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)
Busoniu, L., Babuska, R., Schutter, B.D.: A Comprehensive Survey of Multiagent Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)
Chun-Gui, L., Meng, W., Qing-Neng, Y.: A Multi-agent Reinforcement Learning using Actor-Critic methods. In: 2008 International Conference on Machine Learning and Cybernetics (2008)
Schuitema, E.: Reinforcement Learning on Autonomous Humanoid Robots (2012)
Watkins, C.J.C.H., Dayan, P.: Q-Learning. Machine Learning 8(3-4), 279–292 (1992)
Boutilier, C.: Planning, learning and coordination in multiagent decision processes. In: The Sixth Conference on Theoretical Aspects of Rationality and Knowledge, TARK 1996 (1996)
Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems. In: National Conference on Artificial Intelligence, AAAI 1998 (1998)
Busoniu, L., Schutter, B.D., Babuska, R.: Multiagent reinforcement learning with adaptive state focus. In: 17th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2005, Brussels, Belgium (2005)
Bowling, M., Veloso, M.: Multiagent Learning Using a Variable Learning Rate. Artificial Intelligence 136, 215–250 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sari, S.C., Prihatmanto, A.S., Kuspriyanto (2013). Multi Agent Reinforcement Learning for Gridworld Soccer Leadingpass. In: Omar, K., et al. Intelligent Robotics Systems: Inspiring the NEXT. FIRA 2013. Communications in Computer and Information Science, vol 376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40409-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-40409-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40408-5
Online ISBN: 978-3-642-40409-2
eBook Packages: Computer ScienceComputer Science (R0)