  Reinforcement Learning (RL)-based control system has received considerable
attention in recent decades. However, in many real-world problems, such as
Batch Process Control, the environment is uncertain, which requires expensive
interaction to acquire the state and reward values. In this paper, we present a
cost-efficient framework, such that the RL model can evolve for itself in a
Virtual Space using the predictive models with only historical data. The
proposed framework enables a step-by-step RL model to predict the future state
and select optimal actions for long-sight decisions. The main focuses are
summarized as: 1) how to balance the long-sight and short-sight rewards with an
optimal strategy; 2) how to make the virtual model interacting with real
environment to converge to a final learning policy. Under the experimental
settings of Fed-Batch Process, our method consistently outperforms the existing
state-of-the-art methods.
