Learning More from the Past: Offline Batch RL

ABSTRACT: There is a huge opportunity for enhancing evidence-driven decision making by leveraging the increasing amount of data collected about decisions made, and their outcomes. Reinforcement learning is a natural framework to capture this setting, but online reinforcement learning may always be feasible for higher stakes domains like healthcare or education. In this talk I will discuss our work on offline, batch reinforcement learning, and the progress we have made in techniques that can work efficiently with limited data, and under limited assumptions about the domain.

BIO: Emma Brunskill is an associate professor in the Computer Science Department at Stanford University. Her lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill and her group’s work has been honored by early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research (1 of 7 worldwide) ) and several best research paper nominations (CHI, EDMx3) and awards (UAI, RLDM, ITS).

https://arxiv.org/abs/2007.08202

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Zoom Passcode information is also available upon request to Katherine Godwin ([email protected]).