UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning

Author:

Wang Baolai1ORCID,Li Shengang1ORCID,Gao Xianzhong2ORCID,Xie Tao1ORCID

Affiliation:

1. College of Computer, National University of Defense Technology, Changsha 410073, China

2. College of Aerospace Science, National University of Defense Technology, Changsha 410073, China

Abstract

With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers’ attention. However, the situation faced by the UAV swarm has substantial uncertainty and dynamic variability. The state space and action space increase exponentially with the number of UAVs, so that autonomous decision-making becomes a difficult problem in the confrontation environment. In this paper, a multiagent reinforcement learning method with macro action and human expertise is proposed for autonomous decision-making of UAVs. In the proposed approach, UAV swarm is modeled as a large multiagent system (MAS) with an individual UAV as an agent, and the sequential decision-making problem in swarm confrontation is modeled as a Markov decision process. Agents in the proposed method are trained based on the macro actions, where sparse and delayed rewards, large state space, and action space are effectively overcome. The key to the success of this method is the generation of the macro actions that allow the high-level policy to find a near-optimal solution. In this paper, we further leverage human expertise to design a set of good macro actions. Extensive empirical experiments in our constructed swarm confrontation environment show that our method performs better than the other algorithms.

Funder

Postgraduate Scientific Research Innovation Project of Hunan Province

Publisher

Hindawi Limited

Subject

Aerospace Engineering

Reference25 articles.

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