AAAI Publications, Eighth Artificial Intelligence and Interactive Digital Entertainment Conference

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CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games
Ulit Jaidee, Hector Munoz-Avila

Last modified: 2012-10-07


We present CLASSQ-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASSQ-L uses a single table for each class  of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASSQ-L against a variety of opponents.


computer games, reinforcement learning, machine learning

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