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Constrained Multi-Agent Reinforcement Learning in Imagination

2026-10-15 06:09:04
Raz Shmueli, Shahaf S. Shperberg
arXiv_AI
       
Abstract

Predicting cost in a multi-agent world model moves most constrained policy updates into imagination: the stronger model-free baseline needs $10$ to $20$ times CA-MAMBA's interactions on the median SMAC configuration, and CA-MAMBA earns $2.8$ to $12$ times the baselines' MAMuJoCo reward at equal budget. In controlled tests on one task per benchmark, pricing the model's cost advantages at parity more than halves a cost-blind learner's cost and meets a tight continuous-control limit the learned multiplier misses, suggesting the model's cost estimates are not the bottleneck.

Abstract (translated)

URL

https://arxiv.org/abs/2610.21319

PDF

https://arxiv.org/pdf/2610.21319.pdf

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