The main drawback to reinforcement learning is that it can't be used in some real-life applications. That's because in the process of training themselves, computers initially try just about anything and everything before eventually stumbling on the right path. This initial trial-and-error phase can be problematic for certain applications, such as climate-control systems where abrupt swings in temperature wouldn't be tolerated.

The CSEM engineers have developed an approach that overcomes this problem. They showed that computers can first be trained on extremely simplified theoretical models before being set to learn on real-life systems. That means that when the computers start the machine-learning process on the real-life systems, they can draw on what they learned previously on the models.

The computers can therefore get on the right path quickly without going through a period of extreme fluctuations. The engineers' research has just been published in IEEE Transactions on Neural Networks and Learning Systems."It's like learning the driver's manual before you start a car," says Pierre-Jean Alet, head of smart energy systems research at CSEM and a co-author of the study. "With this pre-training step, computers build up a knowledge base they can draw on so they aren't flying blind as they search for the right answer."