Accelerating process synthesis with reinforcement learning
Transfer learning from multi-fidelity simulations and variational autoencoders
Qinghe Gao (TU Delft - ChemE/Process Systems Engineering)
Haoyu Yang (Student TU Delft)
Maximilian F. Theisen (Student TU Delft)
A.M. Schweidtmanna (TU Delft - ChemE/Process Systems Engineering)
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Abstract
Reinforcement learning has shown some success in automating process design by integrating data-driven models that interact with process simulators to learn to build process flowsheets iteratively. However, one major challenge in the learning process is that the reinforcement learning agent demands numerous process simulations in rigorous process simulators, thereby requiring long simulation times and expensive computational power. We propose employing transfer learning to enhance the reinforcement learning process in process design. This study examines two transfer learning strategies: (i) transferring knowledge from shortcut process simulators to rigorous simulators, and (ii) transferring knowledge from process variational autoencoders (VAEs). Our findings reveal that appropriate transfer learning can significantly improve both learning efficiency and convergence scores. However, transfer learning can also negatively impact the learning process when there are substantial discrepancies in decision range and reward function. This suggests that pre-trained process data should match the complexity of the fine-tuning task.
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