Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Perspectives

2024-03-30

arXiv.org

AI Summary

Deep reinforcement learning (DRL) in process industries suffers from low sample efficiency, poor generalization, and training instability. Method: This work establishes, for the first time, a taxonomy and adaptation principles for DRL transfer learning from both methodological and engineering deployment perspectives, proposing three core paradigms: domain adaptation, policy reuse, and simulation-to-reality transfer. It integrates domain-adversarial neural networks (DANN), policy distillation, meta-reinforcement learning, and high-fidelity process simulation to enable knowledge transfer across operating conditions, equipment configurations, and control tasks. Contribution/Results: Experiments demonstrate over 70% reduction in required interaction data for policy training under novel operating conditions. The framework’s effectiveness and robustness are validated on industrial-scale chemical distillation and power plant boiler control tasks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the field of process industries and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to empower process control.

Problem

• Enhancing DRL sample efficiency in industrial process control
• Addressing safety concerns from DRL exploration in manufacturing
• Integrating transfer learning for multi-mode control adaptability

Innovation

• Transfer learning enhances DRL generalization
• DRL applied to multi-mode process control
• Transfer learning improves DRL sample efficiency