Multiagent reinforcement learning in extensive form games with complete information
Ali Akramizadeh, Mohammad Bagher Menhaj, Ahmad Afshar · 2009
Recent developments in multiagent reinforcement learning, mostly concentrate on normal form games or restrictive hierarchical form games. In this paper, we use the well known Q-learning in extensive form games which agents have a fixed priority in action selection. We also introduce a new concept called associative Q-values which not only can be used in action selection, leading to a subgame perfect equilibrium, but also can be used in update rule which is proved to be convergent. Associative Q-values are the expected utility of an agent in a game situation which is an estimate of the value of the subgame perfect equilibrium point.