[AI Seminar] AI Seminar sponsored by Apple -- David Abel (Brown) -- October 31

[Ellen Vitercik]

I've attached a schedule for David's visit. Please sign up for a slot if
you would like to meet with him.

https://docs.google.com/spreadsheets/d/1SPN4ZwO6w0b7hFHdg4cJuen6HtUojy-L4Oh59UpquwY/edit?usp=sharing

Best,
Ellen Vitercik

On Sun, Oct 29, 2017 at 2:13 AM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> 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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