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

[Adams Wei Yu]

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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