Reinforcement learning: a gentle introduction and industrial application
Reinforcement learning learns complex processes autonomously. No big data sets with the “right” answers are needed; the algorithms learn by experimenting. By using reinforcement learning, robots learn to walk, beat the world champion in Go, or fly a helicopter.
This talk shows “how” and “why” reinforcement learning algorithms work in an intuitive fashion, illustrating their inner-workings by the way a child learns to play a new game. Dr. Christian Hidber shows what it takes to rephrase a real world problem as a reinforcement learning task and takes a look at the challenges to bring it into production on 7000 clients in 42 countries all around the world.
The industrial application is based on siphonic roof drainage systems. It warrants that large buildings like stadiums, airports, or 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. Using reinforcement learning he was able to reduce the fail rate of the existing solution – based on classic supervised learning – by more than 70%.
Dr. Christian Hidber Christian is a consultant at bSquare with a focus on Machine Learning, .Net development, and Azure, and an international conference speaker. He has a PhD in computer algebra from ETH Zurich and did a postdoc at UC Berkeley where he researched online data mining algorithms. Currently, he applies machine learning to industrial hydraulics simulations.