Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

Won Joon Yun; Byungju Lim; Soyi Jung; Young-Chai Ko; Jihong Park; Joongheon Kim; Mehdi Bennis

Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

Multiagent Systems 2021-05-25 v1 Artificial Intelligence Machine Learning

Authors: Won Joon Yun , Byungju Lim , Soyi Jung , Young-Chai Ko , Jihong Park , Joongheon Kim , Mehdi Bennis

Abstract

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.

Keywords

uav optimization reinforcement learning multi-agent reinforcement learning

Cite

@article{arxiv.2105.10716,
  title  = {Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication},
  author = {Won Joon Yun and Byungju Lim and Soyi Jung and Young-Chai Ko and Jihong Park and Joongheon Kim and Mehdi Bennis},
  journal= {arXiv preprint arXiv:2105.10716},
  year   = {2021}
}

Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

Abstract

Keywords

Cite

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