We’ve been running a reading group on Reinforcement Learning (RL) in my lab the last couple of months, and recently we’ve been looking at a very entertaining simulation for testing RL strategies, ye’ old cat vs mouse paradigm. There are a number of different RL methods you can use / play with in that tutorial, but for this I’m only going to talk about Q-learning. The code implementation I’ll be using is all in Python, and the original code comes from one of our resident post-docs, Terry Stewart, and can be garnered from his online RL tutorial. The code I modify here is based off of Terry’s code and modified by Eric Hunsberger, another PhD student in my lab. I’ll talk more about how it’s been modified in another post.
For those unfamiliar, the basic gist of Q-learning is that you have a representation of the environmental…
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