Reinforcement learning in the physical world

Siemens Technology has been working on physical and industrial applications of neural networks and reinforcement learning for more than 20 years. With multiple deployments in various industrial domains (steel, paper, power plants, factory automation, mobility), we have learned about the challenges and constraints that are related to safety-critical environments and real-world applicability. Developing algorithms built on domain expertise, we solve reinforcement learning (RL) tasks with little data but lots of available expert knowledge, and need to establish data and machine learning pipelines as well as deployment strategies. As part of Siemens offerings, RL needs to be reliable, trustworthy, and cost-efficient.

In this talk, we will discuss RL use cases that might impact you every day. Starting with RL-controlled power plant gas turbines, we will introduce typical requirements from the industry and present derived research and software results.

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