  This paper describes an architecture for controlling non-player characters
(NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
Specifically, the DRE-Bot architecture is made up of three reinforcement
learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda})
algorithm. This algorithm enables the NPC to learn through trial and error
building up experience over time in an approach inspired by human learning.
Experimentation is carried to measure the performance of DRE-Bot when competing
against fixed strategy bots that ship with the game. The discount parameter,
{\gamma}, and the trace parameter, {\lambda}, are also varied to see if their
values have an effect on the performance.
