RETALIATE: Learning Winning Policies in First-Person Shooter Games

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.