Reinforcement Learning: Rubik's Cube Case Study

We will use a Rubik's cube as a test environment for reinforcement learning techniques and explore which types of exact and approximation algorithms are useful for making progress on the problem. The first part of the session will cover how to set up the problem environment and common pitfalls. Depending on the time remaining we will explore several of the solution approaches and results so far.

As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.

Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course