Complex control problems arise in the operation of critical infrastructure, including electricity, gas, water and transportation. This project is developing ‘rangl’, an AI competition environment for practitioners (both novice and experienced) to apply classical and machine learning techniques and expert knowledge to UK-centric problems. The project will work with industrial challenge holders to develop insights into the leading classes of solution.
Explaining the science
Infrastructure systems, including electricity, gas, water and transportation, must operate reliably at acceptable cost in the context of ageing infrastructure and new technological possibilities. At the same time a greater amount of measurement and forecast data is becoming available. There is a growing need for appropriate AI controllers to leverage this data. Artificial intelligence, with its speed, scale and accuracy, offers transformative potential in applications to these problems. However, controllers for critical infrastructure should be robustly assessed in an appropriate simulation environment.
This project is developing a competition platform in which challenge environments are formulated in the reinforcement learning framework using the “agent-environment loop”. At each timestep the controller (agent) chooses an action based on the current observation, and the challenge environment returns a new observation and a reward. The aim is to create controllers with intelligent characteristics, capable of handling both quantifiable and unquantifiable uncertainty and encoding expert knowledge. Competition entrants document their controllers, helping to develop insights into the leading classes of solution.
'Rangl' is a competition platform created at The Alan Turing Institute as a new model of collaboration between academia and industry. Through integration with OpenAI Gym, rangl offers a user-friendly environment to develop learning approaches to data-driven control problems. Anybody can propose a rangl challenge, compete in a challenge by designing a controller, or contribute an ‘off-the-shelf’ AI controller for users to customise.
The platforms assess user-submitted algorithms for specific tasks, helping the best classes of solution to emerge; a proven mechanism for realising the potential of AI.