[AI Seminar] AI Seminar sponsored by Apple -- Yifan Wu -- Nov. 6th

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Nov. 6th, at noon in GHC 6115
for AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the website.
On Tuesday, Yifan Wu will give the following talk:

*Title: *The Laplacian in Reinforcement Learning: Learning Representations
without Rewards and Decoders

*Abstract: *The smallest eigenvectors of the graph Laplacian are well-known
to provide a succinct representation of the geometry of a weighted graph.
In reinforcement learning (RL), where the weighted graph may be interpreted
as the state transition process induced by a behavior policy acting on the
environment, approximating the eigenvectors of the Laplacian provides a
promising approach to state representation learning. However, existing
methods for performing this approximation are ill-suited in general RL
settings for two main reasons:  First, they are computationally expensive,
often requiring operations on large matrices. Second, these methods lack
adequate justification beyond simple, tabular, finite-state settings. In
this paper, we present a fully general and scalable method for
approximating the eigenvectors of the Laplacian in a model-free RL context.
We systematically evaluate our approach and empirically show that it
generalizes beyond the tabular, finite-state setting. Even in tabular,
finite-state settings, its ability to approximate the eigenvectors
outperforms previous proposals. Finally, we show the potential benefits of
using a Laplacian representation learned using our method in goal-achieving
RL tasks, providing evidence that our technique can be used to
significantly improve the performance of an RL agent.
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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