Applying Temporal Difference Methods to Machine Learning — Part 3

In this third Part of Applying Temporal Difference Methods to Machine Learning, I will be experimenting with the intra-sequence update variant of TD learning. It is a method where after each time step, the parameters are updated rather than waiting at the end of the sequence.

This post relates to my class project for the Reinforcement Learning class COMP767 I am taking at McGill. For previous work and more information, you can refer to Part 1 and Part 2 of this project.

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Applying Temporal Difference Methods to Machine Learning — Part 1

In this post I detail my project for the course Reinforcement Learning (COMP767) taken at McGill, applying Temporal Difference (TD) methods in a Machine Learning setting.

This concept was first discussed by Sutton when he introduced this family of learning algorithms. I aim to go over what was discussed in the paper and see how it performs on a traditional machine learning problem.

In this part, I will be covering the concepts underlying this application.Read More »