An intelligent control system construction using high-level time Petri net and Reinforcement Learning

Abstract

A hybrid intelligent control system model which combines high-level time Petri net (HLTPN) and Reinforcement Learning (RL) is proposed. In this model, the control system is modeled by HLTPN and system state last time is presented as transitions delay time. For optimizing the transition delay time through learning, a value item is appended to delay time of transition for recording the reward from environment and this value is learned using Q-learning - a kind of RL. Because delay time of transition is continuous, two RL algorithms in continuous space methods are used in Petri Net learning process. Finally, for the purpose of certification of the effectiveness of our proposed system, it is used to model a guide dog robot system which system environment is constructed using radio-frequency identification (RFID). The result of the experiment shows the proposed method is useful and effective.