Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions

Group utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperative behaviour. In this paper we describe what group utility functions are and suggest using them to provide non-player computer game character behaviours. As yet, reinforcement learning techniques have very rarely been used for computer game character specification. Here we show the results of using a group utility function to learn an equilibrium between two computer game characters and compare this against the performance of the two agents learning independently. We also explain how group utility functions could be applied to learn equilibria between groups of agents. We highlight some implementation issues arising from using a commercial computer game engine for multi-agent reinforcement learning ex-