3 papers
cs.AI2026
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requir…
cs.LG2026
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson
We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual varia…
cs.LG2025
Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Bram Silue, Santiago Amaya-Corredor, Patrick Mannion +2
Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from…