---
title: "Towards More Practical Reinforcement Learning"
authors:
  - "Travis Mandel"
doi: ""
url: "https://ijcai.org/proceedings/2015/638"
pdf: "https://ijcai.org/proceedings/2015/0638.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4381-4381"
track: "Doctoral Consortium"
keywords:
---

# Towards More Practical Reinforcement Learning

Travis Mandel

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4381-4381.

## Abstract

Reinforcement Learning is beginning to be applied outside traditional domains such as robotics, and into human-centric domains such as healthcare and education. In these domains, two problems are critical to address: We must be able to evaluate algorithms with a collection of prior data if one is available, and we must devise algorithms that carefully trade off exploration and exploitation in such a way that they are guaranteed to converge to optimal behavior quickly, while retaining very good performance with limited data. In this thesis, I examine these two problems, with an eye towards applications to educational games.