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22 lines
1.4 KiB
Markdown
22 lines
1.4 KiB
Markdown
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Title: Deep Reinforcement Learning
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Description: Combining Reinforcement Learning with Deep Learning
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---
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In the Fall of 2019, I look at integrating demonstration data into a reinforcement learning algorithm in order to make it sample efficient.
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The results are positive and are heavily documented through the following:
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[Honors Thesis](/files/research/honorsthesis.pdf)
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[Honors Defense](/files/research/ExpeditedLearningInteractiveDemo.pptx)
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Thanks to my advisor Dr. Ron Zacharksi and my committee members for all their feedback on my work!
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In the spring of 2019, under the guidance of Dr. Ron Zacharski I practiced several of the modern techniques used in Reinforcement Learning today.
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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))
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I also presented my research (which involved creating an algorithm) at my school's research symposium. ([Slides](/files/research/QEP.pptx)) ([Abstract](abstractspring2019))
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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.
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