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Stable-Predictive Optimistic Counterfactual Regret Minimization

ICML 2019 · Bandits and online learning › Online convex optimization and games · approach: Counterfactual regret minimization · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use OFTRL with the theoretically suggested stepsize instead of vanilla CFRCFR · HUNL subgames 3 and 4, simultaneous updatesexploitabilityHelped
Use OFTRL with the theoretically suggested stepsize instead of vanilla CFRCFR · HUNL subgames 1 and 2, simultaneous updatesexploitabilityHurt
Tune the OFTRL stepsize by dividing it by 10, 100, or 1000 (ablation)Stable-Predictive CFR · HUNL subgames 1 and 2, simultaneous updatesexploitabilityHelped
Use OFTRL instead of vanilla CFRCFR · HUNL subgames 1 and 2, alternating updatesexploitabilityMixed
Use manually tuned OFTRL instead of vanilla CFR (ablation)CFR · HUNL subgames 3 and 4, alternating updatesexploitabilityHurt
Use alternating rather than simultaneous updates (ablation)Stable-Predictive CFR · HUNL subgames 3 and 4exploitabilityHurt
Use DCFR instead of Stable-Predictive CFRHUNL subgames · HUNL subgames 1–4, simultaneous updatesexploitabilityHelped
Use DCFR instead of Stable-Predictive CFRHUNL subgames · HUNL subgames 1–4, alternating updatesexploitabilityHelped
Use stable-predictive regret minimizers rather than regular regret minimizersCFR for two-player zero-sum EFGs · Two-player zero-sum EFGsNash equilibrium convergence rate: O(T^{-1/2}) -> O(T^{-3/4})Helped
Set each local regret minimizer's stability relative to its position in the decision tree and use counterfactual loss predictionsStable-Predictive CFR · Sequential decision processesregret boundHelped

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