Centre for Networked Intelligence

Dear All, Networks Seminar, supported by the Centre for Networked Intelligence, is a technical discussion forum in topics including but not limited to computer networks, machine learning, signal processing, and information theory. The seminar series has a webpage hosted at https://cni.iisc.ac.in/seminars/. You are invited to the following seminar held as part of this series. Title: Sequential Decision-Making under Model Ambiguity Speaker: Dr. Kishan Panaganti, Senior Researcher, Tencent Hunyuan, USA Time: 05:00 PM - 06:00 PM (IST) Date: 12 November 2026 Venue: Online on Zoom Zoom link: https://us06web.zoom.us/j/83388976389?pwd=XcpO3GhLxsR14a7SVbPx33HQQa1jbt.1 Zoom Meeting ID: 833 8897 6389, Pass Code: NSSIISc Webpage Link: https://cni.iisc.ac.in/seminars/2026-11-12/ <https://cni.iisc.ac.in/seminars/2026-11-12/> Abstract: Reinforcement learning systems are often trained in simulators or from data collected under a nominal model, yet deployed in environments with different dynamics, observations, or interacting agents. This talk examines how to make reliable decisions when the model itself is uncertain. I will begin with robust reinforcement learning, where policies optimize performance against an explicit set of plausible models, and show how duality connects robustness to adversarial reweighting. I will then focus on risk-averse quantal response equilibria, which combine risk sensitivity and bounded rationality in multi-agent decision-making. These ideas provide a framework for studying when equilibria can be computed and learned efficiently, including in games where standard equilibrium notions pose computational challenges. I will also discuss extensions to distributionally robust cooperative multi-agent reinforcement learning. Bio: Kishan Panaganti is a researcher at Tencent Hunyuan working on reinforcement learning for foundation models. He was previously a PIMCO Postdoctoral Fellow at Caltech and received his Ph.D. in Electrical and Computer Engineering from Texas A&M University in 2023 and his master’s degree from the Indian Institute of Science in 2017. His research develops theoretical foundations and algorithms for reinforcement learning and multi-agent systems, with a focus on learning reliably under model uncertainty. More details: https://sites.google.com/a/tamu.edu/kpb/home ALL ARE WELCOME. Thank you, CNI Seminar Series Organizing Committee.