SDML Book Club

Introduction to Reinforcement Learning

Reinforcement learning is an interesting branch of machine learning with many recent advances. This is the first in a series of Saturday meetups that will dive into RL. The plan for the May 15 meetup is:

• Explain what reinforcement learning (RL) is and how it differs from supervised and unsupervised learning
• Describe the basic elements of RL
• Define RL in terms of Markov decision processes
• Introduce the notation used in RL
• Show how optimal solutions can be found for simple RL problems
• Share some of the challenges with general RL problems

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 first session will draw most of its material from chapters 1 and 3 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 -- Introduction to Reinforcement 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