8 papers · 1 filter
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Luca Viano, Till Freihaut, Emanuele Nevali +3
In this work, we present the first theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games where both the transition dynamics and each agent's reward f…
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…
Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
Till Freihaut, Luca Viano, Volkan Cevher +2
This paper provides the first expert sample complexity characterization for learning a Nash equilibrium from expert data in Markov Games. We show that a new quantity named the sing…
ShiQ: Bringing back Bellman to LLMs
Pierre Clavier, Nathan Grinsztajn, Raphael Avalos +8
The fine-tuning of pre-trained large language models (LLMs) using reinforcement learning (RL) is generally formulated as direct policy optimization. This approach was naturally fav…
Solving robust MDPs as a sequence of static RL problems
Adil Zouitine, Matthieu Geist, Emmanuel Rachelson
Designing control policies whose performance level is guaranteed to remain above a given threshold in a span of environments is a critical feature for the adoption of reinforcement…
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…