---
Title: Deep Reinforcement Learning 
Description: Combining Reinforcement Learning with Deep Learning
---

In the Fall of 2019, I look at integrating demonstration data into a reinforcement learning algorithm in order to make it sample efficient.

The results are positive and are heavily documented through the following:

[Honors Thesis](/files/research/honorsthesis.pdf)

[Honors Defense](/files/research/ExpeditedLearningInteractiveDemo.pptx)

Thanks to my advisor Dr. Ron Zacharksi and my committee members for all their feedback on my work!

In the spring of 2019, under the guidance of Dr. Ron Zacharski I practiced several of the modern techniques used in Reinforcement Learning today.

I facilitated my learning by creating a [reinforcement learning library](https://github.com/brandon-rozek/rltorch) with implementations of several popular papers. ([Semi-Weekly Progress](weeklyprogress))

I also presented my research (which involved creating an algorithm) at my school's research symposium. ([Slides](/files/research/QEP.pptx)) ([Abstract](abstractspring2019))

In the summer of 2019, I became interested in having the interactions with the environment be in a separate process. This inspired two different implementations, [ZeroMQ](https://github.com/brandon-rozek/zerogym) and [HTTP](https://github.com/brandon-rozek/gymhttp). Given the option, you should use the ZeroMQ implementation since it contains less communication overhead.