Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

Learning algorithms tend to struggle [4]. Hierarchical Reinforcement Learning, or HRL, takes advantage of the hierarchical Pipelines networks are the fulcrum of the oil and gas policy decomposition to exploit underlying problem industries and of gas and water mains. These pipes must structures and simplify the learning of complex tasks. The hierarchical be periodically inspected to guarantee the safety and proper decomposition can be either defined by using prior functioning of the plants. However, inspection is usually knowledge [5], [6], [7], [8], or can be automatically learned a long, expensive and tedious procedure that requires the during training [4], [9], [10]. While the latter category of shut-down of the whole plant and, in the specific case of algorithm does not require expert knowledge for defining industrial pipelines, the removal of the insulation around the the hierarchy, the autonomous discovery of the options often pipes. With metal pipes, the inspection is currently performed leads to sub-optimal policies if additional regularizers are not from the outside using ultrasonic or magnetic probes that used during the learning phase [7], [10].