Competition and Coordination in Stochastic Games

Agent competition and coordination are two classical and most important tasks in multiagent systems. In recent years, there was a number of learning algorithms proposed to resolve such type of problems. Among them, there is an important class of algorithms, called adaptive learning algorithms, that were shown to be able to converge in self-play to a solution in a wide variety of the repeated matrix games. Although certain algorithms of this class, such as Infinitesimal Gradient Ascent (IGA), Policy Hill-Climbing (PHC) and Adaptive Play Q-learning (APQ), have been catholically studied in the recent literature, a question of how these algorithms perform versus each other in general form stochastic games is remaining little-studied. In this work we are trying to answer this question. To do that, we analyse these algorithms in detail and give a comparative analysis of their behavior on a set of competition and coordination stochastic games. Also, we introduce a new multiagent learning alg...

• Type: Conference
• Year: 2007
• Where: AI
• Authors: Andriy Burkov, Abdeslam Boularias, Brahim Chaib-draa
• Keywords: Adaptive Learning Algorithms, AI 2007, Artificial Intelligence, Learning Algorithm, Stochastic Games