CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games
DOI:
https://doi.org/10.1609/aiide.v8i3.12547Keywords:
computer games, reinforcement learning, machine learningAbstract
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.
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Published
2021-06-30
How to Cite
Jaidee, U., & Munoz-Avila, H. (2021). CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 8(3), 8-13. https://doi.org/10.1609/aiide.v8i3.12547
Issue
Section
Artificial Intelligence in Adversarial Real-Time Games