Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly observable. While world models such as Dreamer have demonstrated strong generalization and sample efficiency in single-agent settings, their application to MARL remains limited by an inability to handle teammate-induced uncertainty. We propose a new perspective: treat teammates as structured, learnable components within the agent's world model. We introduce an architecture that factorizes the latent state of a Dreamer-style recurrent state-space model (RSSM) into environment and teammate components, and learns an auxiliary Theory-of-Mind (T...
| Authors | Tomas Leroy-Stone |
|---|---|
| Primary category | cs.MA |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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