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Blind Spots in Reinforcement Learning

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Manage episode 210959047 series 2328414
Content provided by Kyle Polich. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kyle Polich or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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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526 episodes

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Blind Spots in Reinforcement Learning

Data Skeptic

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Manage episode 210959047 series 2328414
Content provided by Kyle Polich. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kyle Polich or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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.

  continue reading

526 episodes

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