Reinforcement learning (RL)

approach was made popular by Google DeepMind in their work on Atari games and Go. An example of RL working in the real world is the task of optimising energy efficiency for cooling Google data centers. Here, an RL system achieved a 40% reduction in cooling costs. An important native advantage of using RL agents in environments that can be simulated (e.g. video games) is that training data can be generated in troves and at very low cost. This is in stark contrast to supervised deep learning tasks that often require training data that is expensive and difficult to procure from the real world.


https://medium.com/@NathanBenaich/6-areas-of-artificial-intelligence-to-watch-closely-673d590aa8aa#.fr334umzj