SDML Book Club

Programming Reinforcement Learning Algorithms

Reinforcement learning is an interesting branch of machine learning with many recent advances. Let's code some RL algorithms and watch them learn! In recent weeks we have discussed dynamic programming, Monte Carlo methods, temporal-difference learning. This session we will look at code implementing:

• Monte Carlo
• SARSA
• Q-learning
• and more, time permitting
  No prerequisites are required, but people may appreciate being familiar with the 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 algorithms used in this session will be based on material from chapters 1 through 7 of the Sutton & Barto book.

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Agenda

• 12:00 - 12:15 pm -- Arrival and socializing
• 12:15 - 1:30 pm -- Programming reinforcement learning algorithms
• 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