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This research addresses the critical challenge of distinguishing causal mechanisms from mere statistical correlations in multi-agent reinforcement learning world models, which is essential for robust performance under distribution shifts. By leveraging policy variance and sequential backdoor conditions, the proposed method enables interpretable environmental dynamics recovery directly from offline demonstrations without needing pre-defined causal graphs. Such advances are pivotal for developing reliable autonomous systems capable of strategic coordination in complex, partially observable environments.
Learning Implicit Causal World Models from Multi-Agent Demonstrations
arXiv:2607.26336v1 Announce Type: cross Abstract: In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem is exacerbated in multi-agent systems where physical transitions are intertwined with strategic agent intents, causing world models to fail under distribution shift. We introduce Implicit Causal World Models to recover environmental dynamics from offline demonstrations without requiring pre-defined causal graphs. By incorporating policy variance, we render world models discoverable via the sequential backdoor condition. Evaluations across coordination tasks (Two-Door, Navigation, and Giveway) demonstrate that these models provide interpretable causal representations under both full and partial observability, with model accuracy scaling directly with interventional strength.
Abstract:In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem is exacerbated in multi-agent systems where physical transitions are intertwined with strategic agent intents, causing world models to fail under distribution shift. We introduce Implicit Causal World Models to recover environmental dynamics from offline demonstrations without requiring pre-defined causal graphs. By incorporating policy variance, we render world models discoverable via the sequential backdoor condition. Evaluations across coordination tasks (Two-Door, Navigation, and Giveway) demonstrate that these models provide interpretable causal representations under both full and partial observability, with model accuracy scaling directly with interventional strength.
Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
arXiv:2607.26336 [cs.LG]
(or arXiv:2607.26336v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.26336 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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