[AI Seminar] ai-seminar-announce Digest, Vol 77, Issue 8

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon at NSH
3305.

On Sun, Oct 29, 2017 at 4:00 PM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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> Today's Topics:
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>    1.  AI Seminar sponsored by Apple -- David Abel (Brown) --
>       October 31 (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 29 Oct 2017 06:13:17 +0000
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Cc: David Abel <david_abel at brown.edu>
> Subject: [AI Seminar] AI Seminar sponsored by Apple -- David Abel
>         (Brown) --      October 31
> Message-ID:
>         <CABzq7eq3rMieCriA4EqYJqtENkjdQNzGP+RCw-vWXJ-BFwqhKw at mail.
> gmail.com>
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>
> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, October 31, at noon in NSH 3305
> 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, David Abel <https://cs.brown.edu/~dabel/> from Brown
> University will
> give the following talk:
>
> Title: Abstraction and Lifelong Reinforcement Learning
>
> Abstract:
>
> Lifelong Reinforcement Learning (RL) presents a diversity of challenges.
> Agents must effectively transfer knowledge across tasks while
> simultaneously addressing exploration, credit assignment, and
> generalization. Abstraction can help overcome these hurdles by compressing
> the state space or empowering the action space of a learning agent, thereby
> reducing the computational and statistical burdens of learning. In this
> talk, I summarize our new results on the effect of abstractions on lifelong
> RL. First, we introduce a new class of value-preserving state abstractions
> whose optimal form can be computed efficiently, improving over existing
> NP-Hardness results. Second, we provide a generic sample bound for
> computing high confidence state abstractions in the lifelong setting.
> Third, we show experimentally that state abstractions only offer marginal
> improvements to lifelong learning on their own, but when paired with action
> abstraction, can enable efficient learning. Further, joint state-action
> abstractions induce a closed operator on representations, thereby yielding
> a simple recipe for constructing and analyzing hierarchies for RL.
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> End of ai-seminar-announce Digest, Vol 77, Issue 8
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