Abstract
In this paper, we investigate the application of reinforcement learning in the learning of chasing behaviours of non-player characters (NPCs). One popular method for encoding intelligent behaviours in game is by scripting where the behaviours on the scene are predetermined. Many popular games have their game intelligence encoded in this manner. The application of machine learning techniques to learn non-player character behaviours is still being explored by game AI researchers. The application of machine learning in games could enhance game playing experience. In this report, we investigate the design and implementation of reinforcement learning to learn the chasing behaviours of NPCs. The design and the simulation results are discussed and further work in this area is suggested.
This is a preview of subscription content, log in via an institution to check access.
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
Bourg, D.M., Seemann, G.: AI for Game Developers. O’Reilly, Sebastopol (2004)
Buşoniu, L., Babuška, R., Schutter, B.D.: A comprehensive survey of multi-agent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)
Champandard, A.J.: AI Game Development. New Riders Publishing, Indianapolis (2004)
Fogel, D.B., Hays, T.J., Johnson, D.R.: A platform for evolving characters in competitive games. In: Proceedings of the Congress on Evolutionary Computation (CEC 2004), pp. 1420–1426 (2004)
Funge, J.D.: Artificial Intelligence for Computer Games: An Introduction. A.K. Peters, Ltd., Wellesley (2004)
Laird, J.E.: It knows what you’re going to do: Adding anticipation to a Quakebot. In: Proceedings of the Fifth International Conference on Intelligent Agents, Montreial, pp. 385-392 (2001)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, London (2010)
Phon-Amnuaisuk, S.: Learning Cooperative Behaviours in Multiagent Reinforcement Learning. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5863, pp. 570–579. Springer, Heidelberg (2009)
Shoham, Y., Powers, R.: Multiagent reinforcement learning: A critical survey. Technical report, Standford University (2003) [online], http://multiagent.stanford.edu/papers/MALearning_ACriticalSurvey_2003_0516.pdf
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A Bradford Book, The MIT Press (1998)
Shoham, Y., Layton-Brown, K.: Multiagent Systems: Algorithmic, Game-theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2009)
Watkins, C.J., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992)
Wooldridge, M.: An Introduction to Multiagent Systems. John Wiley & Sons, Chichester (2002)
Yannakakis, G.N., Hallam, J.: Towards optimizing entertainment in computer games. Applied Artificial Intelligence 21, 933–971 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Phon-Amnuaisuk, S. (2011). Learning Chasing Behaviours of Non-Player Characters in Games Using SARSA. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-20525-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20524-8
Online ISBN: 978-3-642-20525-5
eBook Packages: Computer ScienceComputer Science (R0)