Deep Reinforcement Learning for Multi-Agent Interaction

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• Stefano Albrecht, Edinburgh
• Thursday 17 November 2022, 11:00-12:30
• Hybrid, CBL Seminar room, Department of Engineering, and Zoom https://eng-cam.zoom.us/j/82019956685?pwd=WUNSVVcrdC9IZGxQOHFhSThjUjd2dz09.

If you have a question about this talk, please contact Adrian Weller.

Abstract Our group specialises in developing machine learning algorithms for autonomous systems control, with a particular focus on deep reinforcement learning and multi-agent reinforcement learning. We have a focus on problems of optimal decision making, prediction, and coordination in multi-agent systems. Questions we tackle include: How can a single agent learn to collaborate effectively in a team in which other agents may have diverse types and may enter/leave at any time? How can multiple autonomous 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 Five AI (recently acquired by Bosch) on robust and interpretable motion planning and prediction for autonomous driving.

Bio Dr. Stefano V. Albrecht is Assistant Professor in Artificial Intelligence in the School of Informatics, University of Edinburgh. He leads the Autonomous Agents Research Group (https://agents.inf.ed.ac.uk) which currently consists of 15 members that conduct research into developing machine learning algorithms for autonomous 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 develop AI technologies for autonomous driving. His research has been published in leading AI/ML/robotics conferences and journals, including NeurIPS, ICML , IJCAI, AAAI , UAI, AAMAS , AIJ, JAIR , ICRA, IROS , T-RO. Previously, Dr. Albrecht was a postdoctoral fellow at the University of Texas at Austin working with Prof. Peter Stone. He obtained PhD and MSc degrees in Artificial Intelligence from the University of Edinburgh and a BSc degree in Computer Science from Technical University of Darmstadt.

This talk is part of the Machine Learning @ CUED series.

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