[Colloquium] REMINDER: 11/26 TTIC Colloquium: Sanjay Krishnan, University of Chicago

[Mary Marre via Colloquium]

*When:    *  Monday, November 26th at 11:00 am



*Where:     *TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who:        *Sanjay Krishnan, University of Chicago




*Title:*        “The Curse of Generality”: Deep Reinforcement Learning in
the Wild


*Abstract:* The ubiquity of sequential decision problems throughout
Computer Science makes Deep Reinforcement Learning one of the most exciting
developments of modern AI.  However, realizing the potential of such
general frameworks in real applications has proven to be much more
challenging. I use my work over the last few years on building and
deploying an RL-based relational query optimizer, a core component of
almost every database system, as an exemplary application that highlights
some of the under-appreciated challenges in Deep RL practice. RL algorithms
today: (1) do not fully exploit the structure of software simulators by
collecting data episodically instead of strategically rewinding,
fast-forwarding, skipping, (2) are very sensitive to policy parametrization
especially in cases where there are hierarchical or discontinuous policy
structures, and (3) struggle in "over-actuated" problems where the action
space has significant redundancy. For all three challenges, I present
experimental results illustrating phenomena in practice, our algorithmic
solutions, and highlight the ways in which the same phenomena appear in
other RL domains such as robotics.


*Bio:* Sanjay Krishnan is an Assistant Professor of Computer Science at the
University of Chicago. His research focuses on applications of machine
learning and control theory to computer and cyber-physical systems
problems. Sanjay completed his PhD and Masters Degree at UC Berkeley in
Computer Science in 2018. Sanjay's work has received a number of awards
including the 2016 SIGMOD Best Demonstration award, 2015 IEEE GHTC Best
Paper award, and Sage Scholar award.

*Host:* Matthew Walter <mwalter at ttic.edu>


For more information on the colloquium series or to subscribe to the
mailing list,please see http://www.ttic.edu/colloquium.php
Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Tue, Nov 20, 2018 at 10:01 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:    *  Monday, November 26th at 11:00 am
>
>
>
> *Where:     *TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who:        *Sanjay Krishnan, University of Chicago
>
>
>
>
> *Title:*        “The Curse of Generality”: Deep Reinforcement Learning in
> the Wild
>
>
> *Abstract:* The ubiquity of sequential decision problems throughout
> Computer Science makes Deep Reinforcement Learning one of the most exciting
> developments of modern AI.  However, realizing the potential of such
> general frameworks in real applications has proven to be much more
> challenging. I use my work over the last few years on building and
> deploying an RL-based relational query optimizer, a core component of
> almost every database system, as an exemplary application that highlights
> some of the under-appreciated challenges in Deep RL practice. RL algorithms
> today: (1) do not fully exploit the structure of software simulators by
> collecting data episodically instead of strategically rewinding,
> fast-forwarding, skipping, (2) are very sensitive to policy parametrization
> especially in cases where there are hierarchical or discontinuous policy
> structures, and (3) struggle in "over-actuated" problems where the action
> space has significant redundancy. For all three challenges, I present
> experimental results illustrating phenomena in practice, our algorithmic
> solutions, and highlight the ways in which the same phenomena appear in
> other RL domains such as robotics.
>
>
> *Bio:* Sanjay Krishnan is an Assistant Professor of Computer Science at
> the University of Chicago. His research focuses on applications of machine
> learning and control theory to computer and cyber-physical systems
> problems. Sanjay completed his PhD and Masters Degree at UC Berkeley in
> Computer Science in 2018. Sanjay's work has received a number of awards
> including the 2016 SIGMOD Best Demonstration award, 2015 IEEE GHTC Best
> Paper award, and Sage Scholar award.
>
> *Host:* Matthew Walter <mwalter at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list,please see http://www.ttic.edu/colloquium.php
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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