Distributed hierarchical reinforcement learning in multi-agent adversarial environments

Navid Naderializadeh; Sean Soleyman; Fan Hung; Deepak Khosla; Yang Chen; Joshua G. Fadaie

doi:10.1117/12.2616582

6 June 2022 Distributed hierarchical reinforcement learning in multi-agent adversarial environments

Navid Naderializadeh, Sean Soleyman, Fan Hung, Deepak Khosla, Yang Chen, Joshua G. Fadaie

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130L (2022) https://doi.org/10.1117/12.2616582
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

We develop a hierarchical approach for controlling a team of aircraft in multi-agent adversarial environments. Each individual aircraft is equipped with a high-level agent that is solely responsible for target assignment decisions, and a low-level agent that generates actions based only on the selected target. We use distributed deep reinforcement learning to train the high-level agents, and neuroevolution to train the low-level agents. This approach leverages centralized training for decentralized execution to enable individual autonomy when communication is limited. Simulation results confirm the superiority of our proposed approach as compared to non-hierarchical multi-agent reinforcement learning methods.

Conference Presentation

Citation Download Citation

Navid Naderializadeh, Sean Soleyman, Fan Hung, Deepak Khosla, Yang Chen, and Joshua G. Fadaie "Distributed hierarchical reinforcement learning in multi-agent adversarial environments", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130L (6 June 2022); https://doi.org/10.1117/12.2616582

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