Speaker: Giorgia Ramponi (UZH)
Date: Thursday, 19 November 2026
Time: 10:15–12:00
Venue: Alte Universität, Hörsaal -101
Abstract
Reinforcement learning is moving from isolated agents with well-specified rewards toward systems in which agents interact with humans and with other adaptive agents. This shift makes familiar challenges, like reward misspecification, non-stationarity, credit assignment, and coordination, even more relevant.
In this talk, I will discuss how agents can learn when rewards are unavailable, using demonstrations and human feedback. I will focus in particular on multi-agent settings, where learning must account for strategic behavior and equilibrium, and connect recent results in multi-agent imitation learning and inverse reinforcement learning to emerging challenges in agentic AI and real-world applications.