NeurIPS: Shipra Agrawal on the appeal of reinforcement learning

As deep neural networks have come to dominate AI, the Conference on Neural Information Processing Systems (NeurIPS) has become the most popular conference in the field. And at the most popular conference in the field, one of the most popular topics is reinforcement learning: at this year's NeurIPS, 95 accepted papers use the term in their titles. "Reinforcement learning is very, very powerful, because you can kind of learn anything, adaptively from the feedback, and by exploring the decision space," says Shipra Agrawal, an Amazon Scholar, an assistant professor in Columbia University's Industrial Engineering and Operations Research Department, and an area chair at NeurIPS, who studies reinforcement learning. "In concept, it's very akin to how humans learn, by trial and error, and how they adapt to what they see -- without requiring a loss function and so on, just by some kind of rewards or positive feedback." In reinforcement learning, an agent explores its environment, trying out different responses to different states of affairs, gradually learning a set of policies that will enable it to maximize some reward.