On the usefulness of opponent modeling: the Kuhn Poker case study

The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state information and, in case of concurrent learners, the environment has non-stationary dynamics. These problems could be partially overcome if the policies followed by the other agents were known, and, for this reason, many approaches try to estimate them through the so-called opponent modeling techniques. Although many researches have been devoted to the study of the accuracy of the estimation of opponents' policies, still little attention has been deserved to understand in which situations these model estimations can be actually useful to improve the agent's performance. This paper presents a preliminary study about the impact of using opponent modeling techniques to learn the solution of a POSG. Our main purpose is to provide a measure of the gain in performance that can be obtained by exploiting informat...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Alessandro Lazaric, Mario Quaresimale, Marcello Restelli
• Keywords: ATAL 2008, Intelligent Agents, Kuhn Poker, Opponent Modeling Technique, So-called Opponent Modeling