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@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@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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