Modeling Unit Classes as Agents in Real-Time Strategy Games

Authors

  • Ulit Jaidee Lehigh University
  • Hector Munoz-Avila Lehigh University

Keywords:

Computer Games, agents, learning

Abstract

We present CLASSQL, a multi-agent model for playing real-time strategy games, where learning and control of our own team’s units is decentralized; each agent uses its own reinforcement learning process to learn and control units of the same class. Coordination between these agents occurs as a result of a common reward function shared by all agents and synergistic relations in a carefully crafted state and action model for each class. We present results of CLASSQL against the built-in AI in a variety of maps using the Wargus real-time strategy game.

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Published

2021-06-30

How to Cite

Jaidee, U., & Munoz-Avila, H. (2021). Modeling Unit Classes as Agents in Real-Time Strategy Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9(1), 149-155. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12667