Fri, 29 June 2018
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.
Direct download: blind-spots-in-reinforcement-learning.mp3
Category:data science -- posted at: 8:00am PDT