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Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games

ICML 2021 · Bandits and online learning › Online convex optimization and games · approach: Fast no-regret dynamics in games · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace informed action deviations with full behavioral deviations (ablation)EFR · Goofspiel (2-player), fixedwin frequency: 0.51 -> 0.63Helped
Replace informed action deviations with full behavioral deviations (ablation)EFR · Sheriff, fixedpoints: 0.28 -> 0.91Helped
Use TIPS instead of informed action deviations (ablation)EFR · Goofspiel (2-player), fixedwin frequency: 0.51 -> 0.60Helped
Use TIPS instead of informed action deviations (ablation)EFR · Sheriff, fixedpoints: 0.28 -> 0.82Helped
Use full behavioral deviations instead of TIPS (ablation)EFR · Goofspiel (2-player), fixedwin frequency: 0.60 -> 0.63Helped
Use full behavioral deviations instead of TIPS (ablation)EFR · Sheriff, fixedpoints: 0.82 -> 0.91Helped
Use full behavioral deviations instead of TIPS (ablation)EFR · Goofspiel (3-player), fixedwin frequency: 0.53 -> 0.53No clear effect
Use full behavioral deviations instead of TIPS (ablation)EFR · Sheriff, simultaneouspoints: 0.38 -> 0.38No clear effect
Use TIPS instead of CSPS (ablation)EFR · Goofspiel (3-player), simultaneouswin frequency: 0.91 -> 0.87Hurt
Use TIPS instead of CSPS (ablation)EFR · Sheriff, fixedpoints: 0.61 -> 0.82Helped
Use stronger deviation types rather than weaker types (ablation)EFR variants · Benchmark gamesexpected payoffMixed

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