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Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games

ICML 2021 · Optimization › Nonconvex and smooth optimization · approach: Equilibrium learning in zero-sum games

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Decentralized (each player keeps own critic, no access to other player's actions) single-timescale actor-critic with linear function approximationzero-sum two-player stochastic games · theoretical analysis (no experiments)optimality gap L(K) rate in KNo clear effect
Off-policy reuse of the same data batch instead of fresh batches each iterationdecentralized linear actor critic · theoretical sample complexitysample complexity to reach Nash equilibrium: O(1/ε^4) to O(1/ε^2)Helped

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