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By federating distributional critics and using a Wasserstein barycenter trust region, TR-FedDistRL avoids the dangerous "mean-smearing" that can make federated RL unsafe in critical applications.
A million-scale dataset of globally diverse, cross-modal geo-location pairs, coupled with a novel physical-law-aware network, leapfrogs existing CMGL benchmarks and opens the door to truly universal positioning systems.
