Abstract:
This paper proposes a time-efficient model for solving the Weapon-target assignment (WTA) problem with actor-critic reinforcement learning. While typical heuristic algori...Show MoreMetadata
Abstract:
This paper proposes a time-efficient model for solving the Weapon-target assignment (WTA) problem with actor-critic 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.
Date of Conference: 01-04 October 2023
Date Added to IEEE Xplore: 29 January 2024
ISBN Information: