Facilitating Reinforcement Learning for Process Control Using Transfer
  Learning: Perspectives
Runze Lin Junghui Chen Lei Xie Hongye Su Biao Huang
This paper provides insights into deep reinforcement learning (DRL) for
process control from the perspective of transfer learning. We analyze the
challenges of applying DRL in the field of process industries and the necessity
of introducing transfer learning. Furthermore, recommendations and prospects
are provided for future research directions on how transfer learning can be
integrated with DRL to empower process control.