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Combat Robot Strategy Adaptation Using Multiple Learning Agents

[+] Author Affiliations
Thomas Recchia, Jae Chung, Kishore Pochiraju

Stevens Institute of Technology, Hoboken, NJ

Paper No. IMECE2012-87521, pp. 305-313; 9 pages
  • ASME 2012 International Mechanical Engineering Congress and Exposition
  • Volume 4: Dynamics, Control and Uncertainty, Parts A and B
  • Houston, Texas, USA, November 9–15, 2012
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4520-2
  • Copyright © 2012 by ASME


As robotic systems become more prevalent, it is highly desirable for them to be able to operate in highly dynamic environments. A common approach is to use reinforcement learning to allow an agent controlling the robot to learn and adapt its behavior based on a reward function. This paper presents a novel multi-agent system that cooperates to control a single robot battle tank in a melee battle scenario, with no prior knowledge of its opponents’ strategies. The agents learn through reinforcement learning, and are loosely coupled by their reward functions. Each agent controls a different aspect of the robot’s behavior. In addition, the problem of delayed reward is addressed through a time-averaged reward applied to several sequential actions at once. This system was evaluated in a simulated melee combat scenario and was shown to learn to improve its performance over time. This was accomplished by each agent learning to pick specific battle strategies for each different opponent it faced.

Copyright © 2012 by ASME
Topics: Robots , Warfare



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