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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.

A lot of his team’s early work in reinforcement learning was focused on crafting the features used in algorithms for applications like playing video games or recommending medical treatments. 

“Now, with deep networks, we can automate that process and basically allow the system to discover its features by itself, and that’s proven really powerful,” Bellemare said.

Video games like Atari have been a natural fit for much of this research, because they are simple to set up and make it easy to measure the accuracy and performance of various approaches to developing and executing the algorithms. These environments are easy to run inside of larger reinforcement learning and deep learning infrastructure for developing the fundamentals. Bellemare said they are hoping to transfer these new approaches out to new tasks for real-world problems.

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