82 citations · 98 across the 26 of their papers we have counts for
5 papers · 1 filter
Learning Rewards in Strategic Games
Antoine Bergerault, Cyrille Kone, Giorgia Ramponi
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another. This challenge arises in dom…
Aligning Language Models from User Interactions
Thomas Kleine Buening, Jonas Hübotter, Barna Pásztor +3
Multi-turn user interactions are among the most abundant data produced by language models, yet we lack effective methods to learn from them. While typically discarded, these intera…
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…
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
Raphaël Baur, Yannick Metz, Maria Gkoulta +3
Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly lear…
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum
Navdeep Kumar, Tehila Dahan, Lior Cohen +4
We establish an optimal sample complexity of for obtaining an -optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discoun…