Constrained Multi-Agent Reinforcement Learning in Imagination

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.