Multiagent learning using a variable learning rate
Artificial Intelligence
Multi-agent learning in extensive games with complete information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Alternating-offers bargaining with one-sided uncertain deadlines: an efficient algorithm
Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
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We consider the problem of playing in repeated extensive form games where agents do not have any prior. In this situation classic game theoretical tools are inapplicable and it is common the resort to learning techniques. In this paper, we present a novel learning principle that aims at avoiding oscillations in the agents' strategies induced by the presence of concurrent learners. We apply our algorithm in bargaining, and we experimentally evaluate it showing that using this principle reinforcement learning algorithms can improve their convergence time.