Reinforcement learning and deep learning pairing pushes AI limits

Reinforcement learning and deep learning arose as separate disciplines within AI, but researchers are increasingly finding that pairing the two can deliver promising applications. Deep learning has excelled at tasks like training classifiers for image and speech recognition. Reinforcement learning techniques have excelled at creating AI systems that improve through trial and error to produce game-playing bots and recommendation engines. At the Re•Work Deep Reinforcement Learning Summit in San Francisco, researchers explored how the two approaches are being combined to craft more automated and optimized reinforcement learning algorithms. "In the last six years, we've been really focusing on getting this combination of deep networks and reinforcement learning to be more stable, more reliable, more predictable," said Marc Bellemare, research scientist at Google Brain, in an interview.