Refinement of Approximate Reasoning-Based Controllers by Reinforcement Learning

Abstract

Previous reinforcement learning models for learning control do not use existing knowledge of a physical system's behavior, but rather train the network from scratch. The learning process is usually long, and even after the learning is completed, the resulting network can not be easily explained. On the other hand, approximate reasoning-based controllers provide a clear understanding of the control strategy but can not learn from experience. In this paper, we introduce a new method for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. Unlike previous models, our model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. We demonstrate the application of the new approach to a small but challenging real-world control problem.