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 or incomplete, using demonstrations, human feedback, and interaction. 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 feedback integration to emerging challenges in agentic AI and real-world applications.

Bio: Giorgia is an Assistant Professor with Tenure Track at the Faculty of Business, Economics, and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and an affiliated professor at the Data Science and AI, Computer Science, and Engineering department at Chalmers University of Technology.