Reinforcement learning: A gentle introduction and an industrial application

Description

Reinforcement learning (RL) learns complex processes autonomously like walking, beating the world champion in Go, or flying a helicopter. No big datasets with the “right” answers are needed: the algorithms learn by experimenting.

Christian Hidber shows how and why RL works and demonstrates how to apply it to an industrial hydraulics application with 7,000 clients in 42 countries—illustrating the inner workings by the way a child learns to play a new game. The industrial application stems from siphonic roof drainage systems, which ensure that large buildings like stadiums, airports, and shopping malls do not collapse during heavy rainfalls. Choosing the “right” diameters is difficult, requiring intuition and hydraulic expertise. As of today no feasible, deterministic algorithm is known. Christian explains how using reinforcement learning, he and his team were able to reduce the failure rate of the existing solution (based on classic supervised learning) by more than 70%.