  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.
