[AI Seminar] AI Seminar sponsored by Apple -- Alec Koppel -- Feb 20

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow (Tuesday) noon in *NSH
1507*.

On Sat, Feb 17, 2018 at 12:40 PM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, Feb 20, at noon in *NSH 1507* for
> AI Seminar sponsored by Apple. To learn more about the seminar series,
> please visit the AI Seminar webpage <http://www.cs.cmu.edu/~aiseminar/>.
>
> On Tuesday, Alec Koppel <http://koppel.bitballoon.com/> will give the
> following talk:
>
> Title: Nonparametric Stochastic Methods for Continuous Reinforcement
> Learning
>
> Abstract:
>
> Reinforcement learning is a generic framework to describe an autonomous
> agent seeking to learn behavior sequentially in uncertain environments
> based on rewards. This framework has gained increasing relevance for
> autonomous control, management science, and econometrics. Unfortunately,
> heuristics or intractably complicated tools are still prevalent when state
> and action spaces are continuous. In this talk, we develop new algorithms
> for estimating the value function or action-value function in continuous
> Markov Decision Problems (MDPs). The core of these methods are
> nonparametric (kernelized) extensions of stochastic quasi-gradient methods
> operating in tandem with sparse subspace projections. The resulting tools
> yield the first convergence results for Value or Q-function estimation when
> these functions have an infinite nonlinear parameterization, addressing in
> the affirmative a long-standing open question posed by Tsistiklis and Van
> Roy (1997). We then demonstrate on the classic Mountain Car domain that we
> can obtain comparable performance to existing approaches to TD or Q
> learning with orders of magnitude fewer data samples and interpretable
> representations of the learned functions.
>
> Biography:
>
> Alec Koppel began as a Research Scientist at the U.S. Army Research
> Laboratory in the Computational and Information Sciences Directorate in
> September of 2017. He completed his master's degree in Statistics and
> doctorate in Electrical and Systems Engineering, both at the University of
> Pennsylvania (Penn) in August of 2017. He is also a participant in the
> Science, Mathematics, and Research for Transformation (SMART) Scholarship
> Program sponsored by the American Society of Engineering Education. Before
> coming to Penn, he completed his Master's degree in Systems Science and
> Mathematics and Bachelor's Degree in Mathematics, both at Washington
> University in St. Louis (WashU), Missouri. His research interests are in
> the areas of signal processing, optimization and learning theory. His
> current work focuses on optimization and learning methods for streaming
> data applications, with an emphasis on problems arising in autonomous
> systems. He co-authored a paper selected as a Best Paper Finalist at the
> 2017 IEEE Asilomar Conference on Signals, Systems, and Computers.
>
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