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SUMMARY:Deep Reinforcement Learning for Multi-Agent Interaction  - Stefano
  Albrecht\, Edinburgh
DTSTART:20221117T110000Z
DTEND:20221117T123000Z
UID:TALK192557@talks.cam.ac.uk
CONTACT:Adrian Weller
DESCRIPTION:Abstract\nOur group specialises in developing machine learning
  algorithms for autonomous systems control\, with a particular focus on de
 ep reinforcement learning and multi-agent reinforcement learning. We have 
 a focus on problems of optimal decision making\, prediction\, and coordina
 tion in multi-agent systems. Questions we tackle include: How can a single
  agent learn to collaborate effectively in a team in which other agents ma
 y have diverse types and may enter/leave at any time? How can multiple aut
 onomous agents learn to solve a given task in a scalable and robust way? I
  will also present some of my work done at UK-based self-driving company F
 ive AI (recently acquired by Bosch) on robust and interpretable motion pla
 nning and prediction for autonomous driving.\n\nBio\nDr. Stefano V. Albrec
 ht is Assistant Professor in Artificial Intelligence in the School of Info
 rmatics\, University of Edinburgh. He leads the Autonomous Agents Research
  Group (https://agents.inf.ed.ac.uk) which currently consists of 15 member
 s that conduct research into developing machine learning algorithms for au
 tonomous systems control. Dr. Albrecht is a Royal Society Industry Fellow 
 working with a team at UK-based company Five AI (https://www.five.ai) to d
 evelop AI technologies for autonomous driving. His research has been publi
 shed in leading AI/ML/robotics conferences and journals\, including NeurIP
 S\, ICML\, IJCAI\, AAAI\, UAI\, AAMAS\, AIJ\, JAIR\, ICRA\, IROS\, T-RO. P
 reviously\, Dr. Albrecht was a postdoctoral fellow at the University of Te
 xas at Austin working with Prof. Peter Stone. He obtained PhD and MSc degr
 ees in Artificial Intelligence from the University of Edinburgh and a BSc 
 degree in Computer Science from Technical University of Darmstadt.
LOCATION:Hybrid\, CBL Seminar room\, Department of Engineering\, and Zoom 
 https://eng-cam.zoom.us/j/82019956685?pwd=WUNSVVcrdC9IZGxQOHFhSThjUjd2dz09
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