Sim-to-real transfer in reinforcement learning-based, non-steady-state control for chemical plants
We present a novel framework for controlling non-steady situations in chemical plants to address the behavioural gaps between the simulator for constructing the reinforcement learning-based controller and the real plant considered for deploying the framework. In the field of reinforcement learning,...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2022.2029033 |