The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Learning to compete, compromise, and cooperate in repeated general-sum games
ICML '05 Proceedings of the 22nd international conference on Machine learning
Performance bounded reinforcement learning in strategic interactions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A polynomial-time Nash equilibrium algorithm for repeated games
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
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We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches for repeated games: namely, learning of equilibrium, Pareto-efficient learning, and their variants. The comparison is given from a practical (or engineering) standpoint, i.e., from a point of view of a multiagent system designer whose goal is to maximize the system's overall performance according to a given optimality criterion. Extensive experiments in a wide variety of repeated games demonstrate the efficacy of our approach.