Engineers pre-train AI computers to make them even more powerful

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.