Offline MARL

Offline MARL studies how multiple agents can learn to cooperate or compete from logged trajectories, where their actions affect one another and new mistakes cannot be tested freely. Approaches include behavior cloning, conservative value learning, sequence models, diffusion and flow policies, and world models; theoretical work also studies equilibrium guarantees under dataset coverage assumptions. Methods are evaluated in settings such as SMAC and MPE coordination tasks, continuous-control benchmarks, traffic management, and wireless resource allocation, often with decentralized execution. A central challenge is that policies can take joint actions unlike those in the data, making value estimates unreliable and coordination brittle; current work explores support-aware learning, test-time planning, and stronger generalization while balancing performance, safety, and reliable guarantees.