Theory of Anything Podcast 28: Reinforcement Learning and Q-Learning

Reinforcement Learning is a machine learning algorithm that is a ‘general purpose learner’ (with certain important caveats). It generated a lot of excitement with its stunning victory of Alpha Go against Lee Sedol the world Go champion.   

In this podcast (audio version), we go over the theory of reinforcement learning and how it works to solve any Markov Decision Problem (MDP).   This episode will be particularly useful for Georgia Tech OMSCS students taking classes that deal with Reinforcement Learning (ML4T, ML, RL) as we briefly explain the mathematics of how it works and show some simple examples.  

This episode is best when watched on the Youtube channel, though we’ll release an audio version as well. But the visuals are helpful here. The audio version is abbreviated and removes the mathematical theory and proof.

This episode covers the same material as my blog series on Reinforcement Learning: Part 1, Part 2, Part 3, Part 4.

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