Optimization of a Conventional Tunneling Process Through Offline Reinforcement Learning

Abstract

With emerging data-intensive technologies, industry automation has become promising in different fields, including the construction sector. Reinforcement learning has been applied to optimize conventional tunneling processes to minimize instabilities and excavation time. This study aims to take advantage of offline reinforcement learning through the soft actor-critic method, in which policies are evaluated and improved with offline datasets of the transitions occurring within the environment. The proposed method shows capabilities for encouraging exploration while generating actions, minimizing instabilities during the excavation, and allowing the transfer of this knowledge to different tunneling environments.