Blind Spots in Reinforcement Learning

from Data Skeptic · host Kyle Polich with guest Ramya Ramakrishnan

An intelligent agent trained in a simulated environment may be prone to making mistakes in the real world due to discrepancies between the training and real-world conditions. The areas where an agent makes mistakes are hard to find, known as "blind spots," and can stem from various reasons. In this week's episode, Kyle is joined by Ramya Ramakrishnan, a PhD candidate at MIT, to discuss the idea "blind spots" in reinforcement learning and approaches to discover them.

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This episode is 27 minutes long.

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This episode was published on June 29, 2018.

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