3 papers
cs.AI2026
Multi-agent cooperation through in-context co-player inference
Marissa A. Weis, Maciej Wołczyk, Rajai Nasser +4
Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced…
cs.LG2025
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel +12
Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. Du…
cs.AI2024
Multi-agent cooperation through learning-aware policy gradients
Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald +6
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent lear…