mirror of
https://github.com/Brandon-Rozek/website.git
synced 2024-12-23 17:01:13 +00:00
24 lines
1.4 KiB
Markdown
24 lines
1.4 KiB
Markdown
---
|
|
Title: Deep Reinforcement Learning
|
|
Description: Combining Reinforcement Learning with Deep Learning
|
|
---
|
|
|
|
I am interested in sample-efficient reinforcement learning.
|
|
That is, decreases the number of interactions an agent needs
|
|
with an environment to achieve some goal. In the Fall of 2019,
|
|
I approached this by integrating interactive demonstration
|
|
data into the optimized Deep Q-Networks algorithm.
|
|
|
|
The results are positive and are heavily documented through the following:
|
|
|
|
[Undergraduate Honors Thesis](/files/research/honorsthesis.pdf)
|
|
|
|
[Undergraduate Honors Defense](/files/research/ExpeditedLearningInteractiveDemo.pptx)
|
|
|
|
Thanks to my advisor Dr. Ron Zacharksi and my committee members for all their feedback on my work!
|
|
|
|
The semester prior, I built a [reinforcement learning library](https://github.com/brandon-rozek/rltorch) with implementations of several popular papers. ([Semi-Weekly Progress](weeklyprogress)).
|
|
|
|
I also presented 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.
|