Muhyun Byun, Hyungho Na, and Il-Chul Moon, Time-Efficient Weapon-Target Assignment by Actor-Critic Reinforcement, IEEE Conference on System, Man, and Cybernetics (SMC 2023), Maui, Hawaii, USA, Oct 1-4, 2023

Abstract

This paper proposes a time-efficient model for solving the Weapon-target assignment (WTA) problem with actorcritic reinforcement learning. While typical heuristic algorithms and recently studied artificial neural network methodologies have shown good performance results, the previous approach has not been time-efficient in large-scale WTA problems. This paper utilizes the actor-critic framework to resolve the WTA problem, and this framework enables retrieving solutions 23 times faster than the previous deep Q-network approach. Additionally, we incorporate a recurrent neural network model of gated recurrent units (GRU) to allow agents to learn the latent state-space of the WTA problem. Our experiments demonstrate the solution quality and the time efficiency compared to traditional heuristic methods as well as recent DQN-based RL models. 

@inproceedings{Byun2023,
address = {Mau, Hawaii},
author = {Byun, Muhyun and Na, Hyungho and Moon, Il-Chul},
booktitle = {IEEE Conference on System, Man, and Cybernetics (SMC 2023)},
title = {{Time-Efficient Weapon-Target Assignment by Actor-Critic Reinforcement}},
year = {2023}
}