|Value Iteration Network (2016 NIPS Best Paper Award)|
|Seminar Topic: Value Iteration Network (2016 NIPS Best Paper Award)
Speaker: Yi Wu
Time: Jan. 6, 2:00 p.m. - 3:00 p.m.
Venue: Room 1A-200, SIST Building
We introduce the value iteration network (VIN): a fully differentiable neural network with a ‘planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation.
We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.
Yi Wu is now a 3rd-year Computer Science Ph.D. student at UC Berkeley advised by Prof. Stuart Russell. He received his B.E. from the special pilot class (Yao class) from Institute of Interdisciplinary Information Sciences, Tsinghua University. Yi's research focuses how to effectively incorporate human knowledge into AI models to produce both interpretable and generalizable solution. He is now working on a variety of projects, including probabilistic generative models, probabilistic programming and hierachical reinforcement learning.