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- Boston University Late Modern Philosophy WorkshopAll day
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- 2023 BFA/MA Art Education Thesis Exhibition9:00 am
- SE PhD Prospectus Defense of Vittorio Giammarino11:00 am
- BU Sustainability: Research for People & Planet Webinar Series | Daily Heat and Firearm Violence in 100 US Cities11:00 am
- College of Engineering MechE Spring Seminar Series: Marcia O'Malley 11:00 am
- BU SPH The Future of Population Health (Part 3): Policy, Governance, and Structural Determinants of Health12:00 pm
- Missionaries and Colonialism12:00 pm
- BU Arts Initiative: Out in STE(A)M Luncheon 12:00 pm
- CISS Undergraduate Research Intern Showcase12:00 pm
- MSE PhD Prospectus Defense of Anton Resing12:30 pm
- Young Scholars Initiative Conference on Debt Sustainability1:00 pm
- Philosophy Seminar Series Talk by Prof. Juliet Floyd2:00 pm
- Coffee & Conversation3:00 pm
- CISE Seminar: Pierre Dupont, Boston Children’s Hospital & Harvard Medical Lab3:00 pm
- MSE Talk with Matthieu Fortin-Deschenes3:00 pm
- Graduate Students Support Group3:00 pm
- Complicated Relationships and Sexual Assault Survivors Group3:30 pm
- MA Art Education Reception - School of Visual Arts5:00 pm
- BU Hillel: GradHillel Shabbat Dinner 6:00 pm
- BU HIllel: Israeli Shabbat6:00 pm
- Dontrell, Who Kissed The Sea7:30 pm
- Switzerland7:30 pm
- Art House7:30 pm
- Alcina7:30 pm
- BU Dance Theater Presents: One Man’s Trash: A Repurposed Circus7:30 pm
SE PhD Prospectus Defense of Vittorio Giammarino
TITLE: ON THE USE OF EXPERT DATA TO IMPROVE EFFICIENCY IN REINFORCEMENT LEARNING
ABSTRACT:This dissertation examines the integration of expert datasets to enhance the data efficiency of online Deep Reinforcement Learning (DRL) algorithms in the context of large state and action space problems. The focus is on effectively integrating real-world data, including data from biological systems, to accelerate the learning process within the online DRL pipeline. The motivation for this work is twofold: first, the internet provides access to a vast amount of data, such as videos, that demonstrate various tasks of interest, but are not necessarily designed for use in the DRL framework. Leveraging this data to enhance DRL algorithms presents an exciting and challenging opportunity. Second, biological systems exhibit numerous inductive biases in their behavior that enable them to be highly efficient and adaptable learners. Incorporating these mechanisms for efficient learning remains an open question in DRL, and we consider the use of human and animal data as a possible solution to this problem.
Throughout this dissertation, we address important questions such as how prior knowledge can be distilled into RL agents, the benefits of leveraging offline datasets for online RL, and the algorithmic challenges involved in doing so. We present three original works that investigate the use of animal videos to enhance RL learning performance, develop a framework to learn bio-inspired foraging policies using human data, and propose an online algorithm for performing hierarchical imitation learning in the options framework.
Our research demonstrates the effectiveness of utilizing offline datasets to improve the efficiency and performance of online DRL algorithms, providing valuable insights into how we can accelerate the learning process for complex tasks.
COMMITTEE: AdvisorIoannis Paschalidis, SE, ECE; Eshed Ohn-Bar, ECE; Wenchao Li, SE, ECE; Roberto Tron, SE, ME
When | 11:00 am to 1:00 pm on Friday, April 28, 2023 |
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Location | 8 Saint Mary's Street, RM 442 |