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Adversarial Policy Learning in Two-player Competitive Games

ICML 2021 · Multi-agent systems and games › Games and equilibria · approach: Opponent modeling in competitive games · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use value-difference objective (maximize adversary value minus victim value, monotonic surrogate) instead of PPO on adversary reward aloneadversarial agent training · StarCraft IIadversary winning rate: 98% vs 31%Helped
Use value-difference objective instead of PPO on adversary reward aloneadversarial agent training · MuJoCo games (You-Shall-Not-Pass, Kick-And-Defend, Sumo-Humans)adversary winning rate: higher winning rate in all three gamesHelped
Use value-difference objective instead of PPO on adversary reward aloneadversarial agent training · Sumo-Antsadversary winning rateNo clear effect
Use value-difference objective instead of PPO on adversary reward aloneadversarial agent training · Sumo-Antsnon-loss rate: about 88% non-loss for proposedHelped
Use value-difference objective instead of PPO on adversary reward aloneadversarial agent training · StarCraft IIvictim win plus tie rate after blinding (adversary reliance on observation): 24% vs 98% for baseline after blindingMixed
Retrain victim against the proposed adversary (adversarial retraining)victim agent · StarCraft IIvictim win plus tie rate vs baseline adversary: 89% vs 4% for baseline-retrained victim against our adversaryHelped
Retrain victim with the proposed adversary instead of baseline adversaryvictim agent · Kick And Defendvictim win plus tie rate vs our adversary: 68.0 vs 35.0Helped
Retrain victim against adversary; check regular-agent performancevictim agent · regular agents (Kick And Defend, You Shall Not Pass, Sumo Humans, Sumo Ants, StarCraft II)win rate vs regular agents: negligible influenceNo clear effect
Drop monotonic property (no-monotonic variant) of the value-difference objective (ablation)adversarial agent training · not reported quantitativelyadversary winning rateHurt
Use only victim-reward term as objective instead of value difference (ablation)adversarial agent training · not reported quantitativelyadversary winning rateHurt

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