<div dir="ltr">A gentle reminder that the talk will happen tomorrow (Tuesday) noon at NSH 3305.</div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Oct 29, 2017 at 4:00 PM,  <span dir="ltr"><<a href="mailto:ai-seminar-announce-request@cs.cmu.edu" target="_blank">ai-seminar-announce-request@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send ai-seminar-announce mailing list submissions to<br>
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Today's Topics:<br>
<br>
   1.  AI Seminar sponsored by Apple -- David Abel (Brown) --<br>
      October 31 (Adams Wei Yu)<br>
<br>
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Message: 1<br>
Date: Sun, 29 Oct 2017 06:13:17 +0000<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Cc: David Abel <<a href="mailto:david_abel@brown.edu">david_abel@brown.edu</a>><br>
Subject: [AI Seminar] AI Seminar sponsored by Apple -- David Abel<br>
        (Brown) --      October 31<br>
Message-ID:<br>
        <<a href="mailto:CABzq7eq3rMieCriA4EqYJqtENkjdQNzGP%2BRCw-vWXJ-BFwqhKw@mail.gmail.com">CABzq7eq3rMieCriA4EqYJqtENkjd<wbr>QNzGP+RCw-vWXJ-BFwqhKw@mail.<wbr>gmail.com</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
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Dear faculty and students,<br>
<br>
We look forward to seeing you next Tuesday, October 31, at noon in NSH 3305<br>
for AI Seminar sponsored by Apple. To learn more about the seminar series,<br>
please visit the AI Seminar webpage <<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, David Abel <<a href="https://cs.brown.edu/~dabel/" rel="noreferrer" target="_blank">https://cs.brown.edu/~dabel/</a>> from Brown<br>
University will<br>
give the following talk:<br>
<br>
Title: Abstraction and Lifelong Reinforcement Learning<br>
<br>
Abstract:<br>
<br>
Lifelong Reinforcement Learning (RL) presents a diversity of challenges.<br>
Agents must effectively transfer knowledge across tasks while<br>
simultaneously addressing exploration, credit assignment, and<br>
generalization. Abstraction can help overcome these hurdles by compressing<br>
the state space or empowering the action space of a learning agent, thereby<br>
reducing the computational and statistical burdens of learning. In this<br>
talk, I summarize our new results on the effect of abstractions on lifelong<br>
RL. First, we introduce a new class of value-preserving state abstractions<br>
whose optimal form can be computed efficiently, improving over existing<br>
NP-Hardness results. Second, we provide a generic sample bound for<br>
computing high confidence state abstractions in the lifelong setting.<br>
Third, we show experimentally that state abstractions only offer marginal<br>
improvements to lifelong learning on their own, but when paired with action<br>
abstraction, can enable efficient learning. Further, joint state-action<br>
abstractions induce a closed operator on representations, thereby yielding<br>
a simple recipe for constructing and analyzing hierarchies for RL.<br>
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Subject: Digest Footer<br>
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End of ai-seminar-announce Digest, Vol 77, Issue 8<br>
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