[Lisa_seminaires] [mila-tous] [Tea Talk] Glen Berseth (UBC) July 20 2018 10:30 AA3195

xavier bouthillier xavier.bouthillier at gmail.com
Ven 20 Juil 10:24:51 EDT 2018


The link for the streaming is the following one:
https://mila.bluejeans.com/809027115/webrtc

On Tue, Jul 17, 2018 at 6:00 AM, Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> This week we have *Glen Berseth * from * UBC*, who will be interning with
> Chris Pal, giving a talk on *July 20 2018* at *10:30* in room *AA3195*
>
> Will this talk be streamed <https://mila.bluejeans.com/1365483656/webrtc>?
> Yes!
> Want to meet with Glen? Glen is only available until 2pm on Friday, so
> send him an email directly (gberseth at cs.ubc.ca) and perhaps invite him to
> lunch :)
>
> Given how many tea talks we've had, it should be a breeze to find your way
> by moving your legs and walking to come to this tea talk!
> Michael
>
> *P.S.* We still don't have a speaker for next week's talk, so if you're
> interested in giving a talk please slack or email me!
>
> *TITLE* Scalable Deep Reinforcement Learning for Physics-Based Motion
> Control
>
> *KEYWORDS *hierarchical RL,  meta-learning, multi-task learning
>
> *ABSTRACT*
> Motion control in physics-based animation is challenging due to complex
> dynamics and discontinuous contacts. Many previous control methods that
> produce walking motions are very stiff, only work in particular
> environments and require significant manual tuning to get functioning. In
> this work, we progress the state-of-the-art in physics-based character
> animation in a number of directions using machine learning methods. We
> present three contributions that build upon the current research on motion
> control using deepRL. First, we show that decomposing tasks into a
> hierarchy increase learning efficiency by operating across multiple time
> scales on a complex locomotion and navigation task. Second, we investigate
> improved action exploration methods to sample more promising actions on
> robots and in simulation using forward dynamics distributions. This
> sampling strategy has been shown to improve sample efficiency for a number
> of problems, including many from the OpenAI Gym. Last, we consider a new
> algorithm to progressively learn and integrate new skills producing a
> robust and multi-skilled physics-based controller. This algorithm combines
> the skills of experts together and then applies transfer learning methods
> to initialize and accelerate the learning of new skills.
>
> *BIO* Glen Berseth is a PhD candidate at the Department of Computer
> Science at the University of British Columbia where he works on
> reinforcement learning, machine learning and motion planning. He received
> the BSc degree in Computer Science from York University in 2012 and his MSc
> from York University under the supervision of Petros Faloutsos in 2014 for
> optimization and authoring crowd simulations. He has published in a wide
> range of areas including computer animation, machine learning and robotics
> and is an NSERC scholarship award holder.
>
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