4 papers
Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback
Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +2
We consider robustness against data corruption in offline multi-agent reinforcement learning from human feedback (MARLHF) under a strong-contamination model: given a dataset of…
AgenticRed: Evolving Agentic Systems for Red-Teaming
Jiayi Yuan, Jonathan Nöther, Natasha Jaques +1
While recent automated red-teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on human-specified workflows. This dependen…
Reinforcement Learning for Durable Algorithmic Recourse
Marina Ceccon, Alessandro Fabris, Goran Radanović +2
Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g.,…
Independent Learning in Performative Markov Potential Games
Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın +2
Performative Reinforcement Learning (PRL) refers to a scenario in which the deployed policy changes the reward and transition dynamics of the underlying environment. In this work,…