Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2211.01266,
title = {Knowing the Past to Predict the Future: Reinforcement Virtual Learning},
author = {Peng Zhang and Yawen Huang and Bingzhang Hu and Shizheng Wang and Haoran Duan and Noura Al Moubayed and Yefeng Zheng and Yang Long},
journal= {arXiv preprint arXiv:2211.01266},
year = {2022}
}