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SDML Book Club

Temporal-Difference Learning

Reinforcement learning is an interesting branch of machine learning with many recent advances. Temporal-difference learning is one of the key algorithms in RL. This session will cover:

  • Review what reinforcement learning is and the notation used in RL
  • Explain temporal-difference learning, and introduce the base TD(0) algorithm
  • Show SARSA, an on-policy TD algorithm
  • Present Q-learning, an off-policy TD algorithm
  • Discuss other issues around TD learning
    No prerequisites are required, but people may appreciate being familiar with the introduction to reinforcement learning material, available on our GitHub repo: https://github.com/SanDiegoMachineLearning/bookclub

The majority of the content will be pulled from Reinforcement Learning: An Introduction (second edition) by Richard Sutton and Andrew Bartow. The book isn't the easiest to find right now. The hardcover on Amazon appears to be a knockoff. You may be able to find it elsewhere. You can find free copies of the book online, and one of the places is here: http://incompleteideas.net/book/the-book.html

This session will draw most of its material from chapter 6 of the Sutton & Barto book. Attendees are welcome to either read the chapters before the event and bring questions or discussion items, or use the meetup as a primer and read the chapters afterward. And everyone is also welcome to participate even if they don't plan to do the reading.

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Agenda

  • 12:00 - 12:15 pm -- Arrival and socializing
  • 12:15 - 1:30 pm -- Temporal-difference learning
  • Time permitting -- Breakout discussions

Links to chapter notes and videos of prior meetups are available on the SDML GitHub repo https://github.com/SanDiegoMachineLearning/bookclub

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Location

This will be an online meetup until further notice.

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Questions?

Join our slack channel or leave a comment below if you have any questions about the group or need clarification on anything.
https://join.slack.com/t/sdmachinelearning/shared_invite/zt-6b0ojqdz-9bG7tyJMddVHZ3Zm9IajJA

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