Comprehensive cooperative deep deterministic policy gradients for multi-agent systems in unstable environment

Dong Xie; Xiangnan Zhong; Qing Yang; Yan Huang

doi:10.1117/12.2519153

10 May 2019 Comprehensive cooperative deep deterministic policy gradients for multi-agent systems in unstable environment

Dong Xie, Xiangnan Zhong, Qing Yang, Yan Huang

Author Affiliations +

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060K (2019) https://doi.org/10.1117/12.2519153
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Nowadays, intelligent unmanned vehicles, such as unmanned aircraft and tanks, are involved in many complex tasks in the modern battlefield. They compose the networked intelligent systems with varying degrees of operational autonomy, which will continue to be used increasingly on the future battlefield. To deal with such a highly unstable environment, intelligent agents need to collaborate to explore the information and achieve the entire goal. In this paper, we will establish a novel comprehensive cooperative deep deterministic policy gradients (C₂DDPG) algorithm by designing a special reward function for each agent to help collaboration and exploration. The agents will receive states information from their neighboring teammates to achieve better teamwork. The method is demonstrated in a real-time strategy game, StarCraft micromanagement, which is similar to a battlefield with two groups of units.

Conference Presentation

Citation Download Citation

Dong Xie, Xiangnan Zhong, Qing Yang, and Yan Huang "Comprehensive cooperative deep deterministic policy gradients for multi-agent systems in unstable environment", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060K (10 May 2019); https://doi.org/10.1117/12.2519153

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