[AI Seminar] ai-seminar-announce Digest, Vol 72, Issue 6

[Adams Wei Yu]

A gentle reminder that the talk is tomorrow (Tuesday) noon in NSH 1507!

On Sat, Mar 18, 2017 at 12:00 PM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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> Today's Topics:
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>    1.  AI Lunch -- Wen Sun -- March 21 (Unusual Room: NSH       1507)
>       (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
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> Message: 1
> Date: Fri, 17 Mar 2017 23:34:07 -0400
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Subject: [AI Seminar] AI Lunch -- Wen Sun -- March 21 (Unusual Room:
>         NSH     1507)
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> Dear faculty and students,
>
> We look forward to seeing you Next Tuesday, March 21, at noon in *NSH 1507*
> for AI lunch. To learn more about the seminar and lunch, please visit the
> AI
>  Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.
>
> On Tuesday, Wen Sun <http://www.cs.cmu.edu/~wensun/> will give a talk
> titled *Differentiable Imitation Learning and Sequential Prediction*.
>
> *Abstract*: Recently, researchers have demonstrated state-of-the-art
> performance on sequential decision making problems (e.g., robotics control,
> sequential prediction) with deep neural networks and Reinforcement Learning
> (RL). However, for some of these problems, oracles that can demonstrate
> good performance are available during training. In this work, we propose
> AggreVaTeD, a policy gradient extension of the Imitation Learning (IL)
> approach of Ross & Bagnell (2014) that can leverage oracles to achieve
> faster and more accurate solutions with less training data than with a
> less-informed RL approaches. Specifically, we provide a comprehensive
> theoretical study of IL that demonstrates we can expect up to exponentially
> lower sample complexity for learning with AggreVaTeD than with RL
> algorithms. Finally, we present two stochastic gradient procedures that
> learn neural network policies for several problems including a sequential
> prediction task as well as various high dimensional robotics control
> problems. Our results and theory indicate that the proposed approach can
> achieve superior performance with respect to the oracle when the
> demonstrator is sub-optimal.
>
> This a joint work with Arun Venkatraman, Geoff Gordon, Byron Boots and Drew
> Bagnell.
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> End of ai-seminar-announce Digest, Vol 72, Issue 6
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