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24 lines
1.4 KiB
Markdown
24 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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I am interested in sample-efficient reinforcement learning.
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That is, decreases the number of interactions an agent needs
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with an environment to achieve some goal. In the Fall of 2019,
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I approached this by integrating interactive demonstration
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data into the optimized Deep Q-Networks algorithm.
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The results are positive and are heavily documented through the following:
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[Undergraduate Honors Thesis](/files/research/honorsthesis.pdf)
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[Undergraduate 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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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)).
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I also presented 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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