Using Kullback-Leibler Divergence to Model Opponents in Poker

Opponent modeling is an essential approach for building competitive computer agents in imperfect information games. This paper presents a novel approach to develop opponent modeling techniques. The approach applies neural networks which are separately trained on different dataset to build K- model clustering opponent models. Kullback- Leibler (KL) divergence is used to exploit a safety mode on opponent modeling. Given a parameter d that controls the max divergence between a model’s centre point and the units belong to it, the approach is proved to provide a lower bound of expected payoff which is above the minimax payoff for correctly clustered players. Even for the players that are incorrectly clustered, the lower bound can also be unlimited approximated with sufficient history data. In our experiments, agent with the novel model shows an improved classification efficiency of opponent modeling comparing with relative researches. And also, the new agent performs better when playing against poker agent HITSZ_CS_13 which participate Annual Computer Poker Competition of 2013.