RETALIATE: learning winning policies in first-person shooter games

Authors:
Megan Smith;Stephen Lee-Urban;Héctor Muñoz-Avila
Affiliations:
Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA;Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA;Department of Computer Science & Engineering, Lehigh University, Bethlehem, PA
Venue:
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Year:
2007

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Abstract

In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team first-person shooter games. RETALIATE has three crucial characteristics: (1) individual BOT behavior is fixed although not known in advance, therefore individual BOTS work as "plugins", (2) RETALIATE models the problem of learning team tactics through a simple state formulation, (3) discount rates commonly used in Q-Iearning are not used. As a result of these characteristics, the application of the Q-learning algorithm results in the rapid exploration towards a winning policy against an opponent team. In our empirical evaluation we demonstrate that RETALIATE adapts well when the environment changes.