Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems

Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talk about the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems.