DeepMind's AI automatically generates reinforcement learning algorithms

In a study published on the preprint server Arxiv.org, DeepMind researchers describe a reinforcement learning algorithm-generating technique that discovers what to predict and how to learn it by interacting with environments. They claim the generated algorithms perform well on a range of challenging Atari video games, achieving "non-trivial" performance indicative of the technique's generalizability. Reinforcement learning algorithms -- algorithms that enable software agents to learn in environments by trial and error using feedback -- update an agent's parameters according to one of several rules. These rules are usually discovered through years of research, and automating their discovery from data could lead to more efficient algorithms, or algorithms better adapted to specific environments. DeepMind's solution is a meta-learning framework that jointly discovers what a particular agent should predict and how to use the predictions for policy improvement.