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This arXiv preprint proposes CA-MAMBA, a model-based approach to constrained multi-agent reinforcement learning that predicts cost inside a learned world model so most policy updates happen in imagination. The authors report 10–20x fewer environment interactions than a stronger model-free baseline on SMAC and 2.8–12x higher reward on MAMuJoCo at equal budget, but the cost-related tests use only one task per benchmark and the results are self-reported and not yet peer reviewed. The more useful takeaway for practitioners is the finding that cost-blind learners can be made much safer by adding a cost signal, which suggests the world model's cost estimates may not be the main limitation.
Constrained Multi-Agent Reinforcement Learning in Imagination
arXiv:2610.21319v1 Announce Type: new 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: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.
Artificial Intelligence (cs.AI)
arXiv:2610.21319 [cs.AI]
(or arXiv:2610.21319v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.21319 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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