  In this paper, we study a digital twin (DT)-empowered integrated sensing,
communication, and computation network. Specifically, the users perform radar
sensing and computation offloading on the same spectrum, while unmanned aerial
vehicles (UAVs) are deployed to provide edge computing service. We first
formulate a multi-objective optimization problem to minimize the beampattern
performance of multi-input multi-output (MIMO) radars and the computation
offloading energy consumption simultaneously. Then, we explore the prediction
capability of DT to provide intelligent offloading decision, where the DT
estimation deviation is considered. To track this challenge, we reformulate the
original problem as a multi-agent Markov decision process and design a
multi-agent proximal policy optimization (MAPPO) framework to achieve a
flexible learning policy. Furthermore, the Beta-policy and attention mechanism
are used to improve the training performance. Numerical results show that the
proposed method is able to balance the performance tradeoff between sensing and
computation functions, while reducing the energy consumption compared with the
existing studies.
