[Lisa_seminaires] [Tea Talk] Bradly Stadie (UC Berkeley) Wed Feb 21 3:00PM PCM Z220

Michael Noukhovitch mnoukhov at gmail.com
Mar 20 Fév 12:20:10 EST 2018


*CORRECTION*: the room is Pavilion Claire McNicoll Z220

On Tue, Feb 20, 2018 at 12:14 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> Sorry for the lateness, please note this talk is a normal tea talk and it
> is *tomorrow!* If you want to meet with the speaker, please email me and
> I'll set it up!
>
> This week we have *Bradly Stadie* from *UC Berkeley* giving a talk on *Wednesday
> Feb 21* at *3:00PM* in room *AA1360*.
>
> See you there!
> Michael
>
> *TITLE* Towards Quickly Learning Agents
>
> *KEYWORDS *Meta-learning, Reinforcement Learning, Imitation Learning
>
>
> *ABSTRACT*We consider the problems of meta reinforcement learning,
> wherein the goal is to train agents that can learn new tasks quickly. We
> suggest 5 major sub-problems that are crucial for enabling quickly learning
> agents: Exploration, Imitation, Hierarchy/Planning, Credit Assignment, and
> Control.
>
> The talk will examine the sub-problems of exploration and imitation and
> their interplay with quickly learning agents in more depth. In particular,
> we examine:
>
> 1) Third Person Imitation: which enables agents to imitate other agents
> that differ significantly from themselves.
> 2) One Shot Imitation: which enables a robot to see a single video of a
> demonstration and copy the demonstrated behavior.
> 3) E-MAML: a meta-RL algorithm that achieves significantly better
> exploration than previous meta-RL algorithms.
>
> *BIO*
> Bradly Stadie is a PhD student at UC Berkeley in Pieter Abbeel’s lab.
> Previously, he spent 2 years working at Open AI under Ilya Sutskever.
> Bradly’s work has focused on the fields of exploration, imitation, and meta
> learning with applications to games, robotics, and causal inference. Prior
> to his interest in AI, Bradly studied mathematics at the University of
> Chicago, where his work focused on representation theory of locally compact
> groups and stochastic differential equations.
>
>
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