3 papers
cs.LG2026
Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
Sumedh J. Dongare, Patrick Weber, Andrea Ortiz +3
Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP)…
cs.AI2026
MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
Lingyi Wang, Rashed Shelim, Walid Saad +1
A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons…
quant-ph2025
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels
Alexander DeRieux, Walid Saad
Collaboration is a key challenge in distributed multi-agent reinforcement learning (MARL) environments. Learning frameworks for these decentralized systems must weigh the benefits…