Real-world Reinforcement Learning in Multi-Agent Systems | Eugene Vinitsky

ICARL Seminar Series - 2024 Spring

Real-world Reinforcement Learning in Multi-Agent Systems.
Seminar by Eugene Vinitsky (NYU / Apple)

Abstract

We investigate how multi-agent learning can enable safe deployment and evaluation of autonomous systems operating in safety-critical, mixed human-robot settings. Using a case study of a 100-vehicle, real-world deployment of RL-based traffic-smoothing autonomous vehicles (AVs), we discuss the challenges of estimating when a controller will successfully bridge the sim-to-real gap. We then discuss our work on building human-like, capable simulated agents using regularized self-play techniques. Finally, we discuss some of the challenges of MARL at scale and the new simulators we are designing to address them.

About the speaker

Eugene Vinitsky is an assistant professor in Transportation Engineering at NYU, a member of the C2SMARTER consortium on congestion reduction, and a part-time research scientist at Apple. He works primarily on multi-agent learning with a focus on its potential use in transportation systems and robotics. At UC Berkeley, where he was advised by Alexandre Bayen, he received his PhD in controls engineering and received an MS and BS in physics from UC Santa Barbara and Caltech respectively. During his PhD he spent time at DeepMind, Tesla Autopilot, and FAIR.

Sponsors

This event is sponsored by InstaDeep and Google DeepMind

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Links
Eugene Vinitsky
Site: www.eugenevinitsky.com
Twitter: twitter.com/EugeneVinitsky

ICARL
Site: icarl.doc.ic.ac.uk
Twitter: twitter.com/ic_arl
YouTube: @ICARLSeminars
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