DG-PG augments policy gradients with descent signals from analytical models to reduce estimator variance from O(N) to O(1), preserve game equilibria, and achieve agent-independent sample complexity while converging on 1500-agent tasks where baselines fail.
Policy invariance under reward transfor- mations: Theory and application to reward shaping
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Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning
DG-PG augments policy gradients with descent signals from analytical models to reduce estimator variance from O(N) to O(1), preserve game equilibria, and achieve agent-independent sample complexity while converging on 1500-agent tasks where baselines fail.