Value-Aware Prediction: Value-Weighted Robust Multi-Agent Coordination under Communication Loss

Kafadar et al. propose Value-Aware MARO, which couples the predictor loss function to the policy's value signal, enabling multi-agent systems to maintain collaborative execution even when communication reliability drops below 40% or is completely interrupted. The method uses GAE to compute actor-critic advantage estimates and separates gradients as importance weights, filters with ReLU to avoid negative advantages causing gradient reversal, and then adjusts its scaling of the prediction loss via a λ hyperparameter, thereby focusing on high-reward intentional dynamics rather than random exploration noise and eliminated policy behaviors.