# Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay

arXiv: https://arxiv.org/abs/2202.00329 · HTML: https://mlsanity.com/paper/2202.00329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV](https://mlsanity.com/paper/2410.04320/) (2024)
- [Enhancing Information Freshness: An AoI Optimized Markov Decision Process](https://mlsanity.com/paper/2409.02424/) (2024)
- [Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games](https://mlsanity.com/paper/2510.07813/) (2025)
- [Efficient Resource Optimization for Split Federated Learning](https://mlsanity.com/paper/2608.17849/) (2026)

## Abstract (arXiv)

This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz's formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.