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| Title | Description |
|---|---|
| Deep Reinforcement Learning | 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:
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 with implementations of several popular papers. (Semi-Weekly Progress)
I also presented my research (which involved creating an algorithm) at my school's research symposium. (Slides) (Abstract)
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 and HTTP. Given the option, you should use the ZeroMQ implementation since it contains less communication overhead.