5 papers
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…
On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
Till Freihaut, Giorgia Ramponi
Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, ident…
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…
Clustered KL-barycenter design for policy evaluation
Simon Weissmann, Till Freihaut, Claire Vernade +2
In the context of stochastic bandit models, this article examines how to design sample-efficient behavior policies for the importance sampling evaluation of multiple target policie…