Learning Strategies through Reinforcement Learning

By Max Candocia


August 31, 2018

This past April I gave a talk on reinforcement learning while visiting UIUC before the Illinois Marathon the following day. The lecture was given to the STAT 385 (Statistics Programming Methods) class taught by James Balamuta, who invites a few guest lecturers to the class per semester to talk about statistics and data science-related topics.

The focus of this talk was reinforcement learning, using the two games I simulated in Python: Machi Koro and Splendor. In contrast to the content I post on the site, the talk was geared more towards the process of working on a project and structuring files/workflow. Since this was an exploratory project, this does does not represent how I would structure data/files in a more serious environment.

Below the embedded video, there are links to the original articles on these topics that I wrote. To view the video on YouTube directly, go to https://www.youtube.com/watch?v=YZtME6NDCAM.

Original Articles

* Note that this article was made a week after the talk was given. The conclusions are largely the same, although the general content is quite different from the video's.


Recommended Articles

Modeling Heart Rate Recovery with Nonlinear Regression

Nonlinear regression models can succeed where linear models fail and highly complex models cannot be interpreted. Using heart rate data I collected from my runs, I demonstrate how my heart rate recovers after stopping as a function of temperature and rest time.

What is my Lottery Ticket Actually Worth?

When you buy a lottery ticket, how much is it worth to you? Is the giant jackpot the main draw, or do you find the other prizes alluring? Using expected utility, we can see that tickets are worth much less than you may already think.