A game-theoretic learning model in multi-agent systems

This paper investigates the problem of learning in a multiagent system that can be applied to telecommunication networks. We model the strategic inter-dependence situation and learning dynamics of self-interested agents in the framework of Markov game with. incomplete information. By combining the fictitious player´s best response strategy and Nash Q-learning´s multi-agent Q-learning, we propose a new multi-agent learning algorithm that can maximize the learning agent´s expected reward and optimize the system-wide performance. We also summarize other algorithms from the game theory and reinforcement learning communities, and compare these algorithms with ours.