Abstract
A cooperative team of agents may perform many tasks better than single agents. The question is how cooperation among self-interested agents should be achieved. It is important that, while we encourage cooperation among agents in a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach based on bidding utilizing reinforcement values acquired through reinforcement learning. We tested and analyzed this approach and demonstrated that a team indeed performed better than the best single agent as well as the average of single agents.
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References
Pollack, J., Blair, A.: Co-evolution in the successful learning of Backgammon strategy. Machine Learning 32(3), 225–240 (1998)
Tesauro, G.: Practical issues in temporal difference learning. Machine Learning 8, 257–277 (1992)
Watkins, C.: Learning with Delayed Rewards. Ph.D Thesis, Cambridge University, Cambridge, UK (1989)
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© 2004 Springer-Verlag Berlin Heidelberg
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Sun, R., Qi, D. (2004). Learning Team Cooperation. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_88
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DOI: https://doi.org/10.1007/978-3-540-30499-9_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
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