Multi-agent Reinforcement Learning Method for a Class of Dilemma Problems
Author:
Affiliation:
1. Graduate School of Science and Technology, Kyoto Institute of Technology
Publisher
The Society of Instrument and Control Engineers
Link
https://www.jstage.jst.go.jp/article/sicetr/51/5/51_352/_pdf
Reference7 articles.
1. 1) L. Busoniu and R. Babuska: Comprehensive Survey of Multiagent Reinforcement Learning, IEEE Transactions on Systems, Man, and Cybernetics — PART C, 38-2, 156/172 (2008)
2. 2) X. Yao and P.J. Darwen: An Experimental Study of N-person Iterated Prisoner's Dilemma Games, Informatica, 18-4, 435/450 (1994)
3. 3) T.W. Sandholm and R.H. Crites: On Multiagent Q-Learning in a Semi-competitive Domain, Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems, 191/205 (1995)
4. 4) T. Makino and K. Aihara: Multi-agent reinforcement learning algorithm to handle beliefs of other agents' policies and embedded beliefs, Proceeding of International Joint Conference on Autonomous Agents and Multiagent Systems, 789/791 (2006)
5. 5) D. Banerjee and S. Sen: Reaching Pareto-optimality in prisoner's dilemma using conditional joint action learning, Autonomous Agents and Multiagent System, 15-1, 91/108 (2007)
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