Google's AI picks which machine learning models will produce the best results

Leave it to the folks at Google to devise AI capable of predicting which machine learning models will produce the best results. In a newly-published paper ("Off-Policy Evaluation via Off-Policy Classification") and blog post, a team of Google AI researchers propose what they call "off-policy classification," or OPC, which evaluates the performance of AI-driven agents by treating evaluation as a classification problem. The team notes that their approach -- a variant of reinforcement learning, which employs rewards to drive software policies toward goals -- works with image inputs and scales to tasks including vision-based robotic grasping. "Fully off-policy reinforcement learning is a variant in which an agent learns entirely from older data, which is appealing because it enables model iteration without requiring a physical robot," writes Robotics at Google software engineer Alexa Irpan. "With fully off-policy RL, one can train several models on the same fixed dataset collected by previous agents, then select the best one."