Hybrid Deep Reinforcement Learning Agent for Online Scheduling and Control for Chemical Batch Plants

This study presents a framework for the implementation of a Deep Reinforcement Learning (DRL) agent for optimal scheduling and control integration on flow-shop batch plants with input variability. The agent is designed to take multiple decisions at every time interval which allows for the integration of scheduling and control. A hybrid agent with multiple decision outputs is used to perform online scheduling and control. To account for the short-term history of the process, the agent approaches the optimization problem as a Partially Observable Markov Decision Process (POMDP). The agent makes use of a set of Long Short-Term Memory cells (LSTM) to correlate sequential states from the environment to be aware of its evolution when taking decisions. To demonstrate the advantages and limitations of the hybrid agent, the method is implemented on a batch plant under variability in the inputs. Results showed that the agent’s policy reacted to the fluctuations in concentration from raw materials. To validate the proposed method, a comparison with an agent trained on an environment with fixed inputs was performed to demonstrate the adaptive behavior of the agent developed with the presented framework.