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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 reinforcementlearners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda})algorithm. This algorithm enables the NPC to learn through trial and errorbuilding up experience over time in an approach inspired by human learning.Experimentation is carried to measure the performance of DRE-Bot when competingagainst fixed strategy bots that ship with the game. The discount parameter,{\gamma}, and the trace parameter, {\lambda}, are also varied to see if theirvalues have an effect on the performance.

DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa({\lambda}) Reinforcement Learners