[Lisa_seminaires] [Tea Talk] Alexander Vezhnevets (Deepmind) Fri Mar 16 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Ven 16 Mar 10:10:50 EDT 2018


*Reminder* this is in 20 minutes!

On Tue, Mar 13, 2018 at 12:24 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> This week we are lucky to have *Alexander Vezhnevets*, research scientist
> at *Deepmind,* giving a talk on *Friday March 16* at *10:30AM* in room
> *AA1360*.
>
> If you want to meet with Alexander in the afternoon, add your name and the
> times you're free here: https://doodle.com/poll/6bbzqikmtx5nuktv
>
> Navigate your way, by walking and taking the elevator, specfically moving
> towards the elevator, taking the elevator down, exit the elevator, and
> making your way to room 1360 to come to this funtastic talk!
> Michael
>
> *TITLE *What we want from (H)RL and other FuN topics
>
> *KEYWORDS *Hierarchical Learning, RL, Compositional Learning
>
>
> *ABSTRACT*Deep Reinforcement learning is making headlines. Superhuman at
> Go and Space Invaders, possibly Starcraft next! Yet most of the state of
> the art, human beating architectures are reactive and data hungry. They
> don’t possess transferable skills, can’t break complex tasks into sub-task,
> plan into the future or find more than one solution to a problem.
> Hierarchical reinforcement learning is an area of research that aims to
> build agents with complex, structured behaviour by endowing them with all
> of these desired properties of intelligence.
>
> In this talk we will review the recent progress in HRL and discuss one
> model - FeUdal Networks - in more details. FeUdal Networks (FuN) is a
> neural network architecture, which learns to decompose its behaviour into
> meaningful primitives and then reuse them to more efficiently acquire new,
> complex behaviours. This allows it to reason on different temporal
> resolutions and thereby improve long-term credit assignment and memory.
>
>
> *BIO*Alexander Vezhnevets is a research scientist at DeepMind working on
> hierarchical RL. Originally from Moscow, he got his PhD in Machine Learning
> from ETH Zurich, where he worked with Joachim Buhmann on structured output
> learning. He then spent two years in lovely Edinburgh working on computer
> vision with Vittorio Ferrari.
>
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