CESG Seminar – Desik Rengarajan

Friday, March 24, 2023
3:50 – 4:50 p.m. (CST)
Zoom (see syllabus or email list for link)

Desik Rengarajan
PhD Candidate, Spring 2023
Dept. of Electrical and Computer Engineering
Texas A&M University

Title: “Enhancing Reinforcement Learning Using Data and Structure”

Talking Points

Challenges in learning in sparse reward environments
Developing RL algorithms that take advantage of sub-optimal demonstration data to learn in sparse reward environments
Developing meta-RL algorithms that take advantage of sub-optimal demonstration data and structure to learn in sparse reward environments

Abstract
In reinforcement learning, reward functions serve as an indirect method of defining the goal of the algorithm. Designing a reward function that accurately captures the task at hand while effectively guiding the learning process can be a difficult challenge, requiring expert domain knowledge and manual fine-tuning. To overcome this, it is often easier to rely on sparse rewards that merely indicate partial or complete task completion. However, this leads to RL algorithms failing to learn an optimal policy in a timely manner due to the lack of fine-grained feedback. During this talk, I will delve into the impact of sparse rewards on reinforcement and meta reinforcement learning and present algorithms that leverage sub-optimal demonstration data to overcome these challenges.

Biography
Desik Rengarajan is a PhD candidate at Texas A&M University’s Department of Electrical and Computer Engineering, where he specializes in reinforcement learning. His research centers on the development of reinforcement learning algorithms that take advantage of side information, such as demonstration data and structure, to enhance the learning process and overcome challenges that arise when implementing RL in real-world situations.