DeepMind taught AI how to multitask using video games

Google's DeepMind team last week revealed a speedy new approach to training deep learning networks that combines advanced algorithms and old school video games. DeepMind, the team responsible for AlphaGo, appears to believe machines can learn like humans do. Using its own DMLab-30 training set, which is built on ID Software's Quake III game and an arcade learning environment running 57 Atari games, the team developed a novel training system called Importance Weighted Actor-Learner Architectures (IMPALA). With IMPALA, an AI system plays a whole bunch of video games really fast and sends the training information from a series of "actors" to a series of "learners." Normally, deep learning networks figure things out like a single gamer traversing a gaming engine.