  This article presents a digital twin (DT)-enhanced reinforcement learning
(RL) framework aimed at optimizing performance and reliability in network
resource management, since the traditional RL methods face several unified
challenges when applied to physical networks, including limited exploration
efficiency, slow convergence, poor long-term performance, and safety concerns
during the exploration phase. To deal with the above challenges, a
comprehensive DT-based framework is proposed to enhance the convergence speed
and performance for unified RL-based resource management. The proposed
framework provides safe action exploration, more accurate estimates of
long-term returns, faster training convergence, higher convergence performance,
and real-time adaptation to varying network conditions. Then, two case studies
on ultra-reliable and low-latency communication (URLLC) services and multiple
unmanned aerial vehicles (UAV) network are presented, demonstrating
improvements of the proposed framework in performance, convergence speed, and
training cost reduction both on traditional RL and neural network based Deep RL
(DRL). Finally, the article identifies and explores some of the research
challenges and open issues in this rapidly evolving field.
