Multiagent learning in adaptive dynamic systems

Classically, an approach to the multiagent policy learning supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all players, would find an interdependent solution called “equilibrium”. Recently, however, certain researchers question the necessity and the validity of the concept of equilibrium as the most important multiagent solution concept. They argue that a “good” learning algorithm is one that is efficient with respect to a certain class of counterparts. Adaptive players is an important class of agents that learn their policies separately from the maintenance of the beliefs about their counterparts’ future actions and make their decisions based on that policy and the current belief. In this paper, we propose an efficient learning algorithm in presence of the adaptive counterparts called Adaptive Dynamics Learner (ADL), which is able to learn an efficient policy over the opponents’ adaptive dynamics rather than ...

• Type: Conference
• Year: 2007
• Where: ATAL
• Authors: Andriy Burkov, Brahim Chaib-draa
• Keywords: Adaptive, Adaptive Dynamics, ATAL 2007, Learning Algorithm