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
Generally Intelligent
00:00
The Intuition for Regression Learning (RL)
In RL, it seems that you get one of two things. You either get that whole thing kind of fails entirely, and you get really, really complicated functions. But somehow, it seems hard to hit that sweet spot with supervised learning. That's interesting. How much does data diversity help? Like, if you were to add a lot more off-wearing data on various types, does that seem to do anything to this problem? Or not really?
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