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
Machine Learning Systems That Emulate Humans Through Imitation Learning Through Supervised Learning
There has been a lot of advances in machine learning systems, both in robotics and in other areas like vision and language. But I think that ultimately we really need machine learning systems that do a good job of going beyond the best that people can do. And that's really the promise of reinforcement learning.
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