6 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…
Risk-seeking conservative policy iteration with agent-state based policies for Dec-POMDPs with guaranteed convergence
Amit Sinha, Matthieu Geist, Aditya Mahajan
Optimally solving decentralized decision-making problems modeled as Dec-POMDPs is known to be NEXP-complete. These optimal solutions are policies based on the entire history of obs…
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…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…