24 citations · 42 across the 17 of their papers we have counts for
15 papers · 1 filter
Rate optimal learning of equilibria from data
Till Freihaut, Luca Viano, Emanuele Nevali +3
We close open theoretical gaps in Multi-Agent Imitation Learning (MAIL) by characterizing the limits of non-interactive MAIL and presenting the first interactive algorithm with nea…
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Matthieu Geist +2
Mean Field Games (MFGs) offer a powerful framework for studying large-scale multi-agent systems. Yet, learning Nash equilibria in MFGs remains a challenging problem, particularly w…
Convergence of regularized agent-state-based Q-learning in POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
In this paper, we present a framework to understand the convergence of commonly used Q-learning reinforcement learning algorithms in practice. Two salient features of such algorith…
Averaging log-likelihoods in direct alignment
Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar +8
To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL…
RRLS : Robust Reinforcement Learning Suite
Adil Zouitine, David Bertoin, Pierre Clavier +2
Robust reinforcement learning is the problem of learning control policies that provide optimal worst-case performance against a span of adversarial environments. It is a crucial in…
Time-Constrained Robust MDPs
Adil Zouitine, David Bertoin, Pierre Clavier +2
Robust reinforcement learning is essential for deploying reinforcement learning algorithms in real-world scenarios where environmental uncertainty predominates. Traditional robust…