RETALIATE: Learning Winning Policies in First-Person Shooter Games

Megan Smith, Stephen Lee-Urban, Hector Munoz-Avila

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 plug-ins, (2) RETALIATE models the problem of learning team tactics through a simple state formulation, (3) discount rates commonly used in Q-learning 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.

Subjects: 1.8 Game Playing; 12.1 Reinforcement Learning

Submitted: Apr 3, 2007


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