Early days of reinforcement learning with Rich Sutton | Michael Littman and Lex Fridman | Summary and Q&A

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December 13, 2020
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Lex Clips
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Early days of reinforcement learning with Rich Sutton | Michael Littman and Lex Fridman

TL;DR

Explore the history of reinforcement learning and its impact on artificial intelligence, from its early beginnings in the 80s to the advancements and challenges faced in recent years.

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Questions & Answers

Q: How did you first become interested in computer science and programming?

I fell in love with computer science when I received a TRS-80 Model 1 computer in 1979. I spent hours programming in BASIC and exploring its capabilities, which sparked my passion for the field.

Q: When did you first learn about neural networks, and in what context?

I learned about neural networks in college through a psychology class, where we discussed their potential applications in understanding human behavior and cognition.

Q: What role did reinforcement learning play in your early exploration of AI?

During my college years, I became intrigued by the concept of teaching programs to learn how to behave. This led me to discover reinforcement learning, and I delved into Rich Sutton's TD (temporal difference) learning paper to understand the principles better.

Q: How did the introduction of Q-learning impact the field of reinforcement learning?

Q-learning introduced the concept of off-policy learning, which allowed systems to learn about the environment while simultaneously figuring out how to behave optimally. This breakthrough opened new possibilities and solved several challenges in reinforcement learning.

Summary & Key Takeaways

  • The interviewee shares their personal journey in the field of artificial intelligence, specifically reinforcement learning, dating back to the 80s when neural networks were emerging.

  • They discuss their early fascination with teaching their home computer to play tic-tac-toe and their introduction to reinforcement learning concepts in college, through psychology and cognitive science classes.

  • The conversation touches on the intersection of neuroscience and cognitive psychology with deep learning, highlighting the unique perspective these disciplines bring to machine learning problems.

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