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Research map / Learning theory and algorithms / Submodular and game algorithms

Solving Large Extensive-Form Games with Strategy Constraints

AAAI 2019 · Learning theory and algorithms › Submodular and game algorithms · approach: Correlated equilibria in sequential games

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Constrained CFR instead of solving the constrained game with an LPTransit game · Transit game, w=8risk-bound deviation: within 0.001Helped
Use Constrained CFR instead of solving the constrained game with an LPTransit game · Transit game, w=8exploitability difference from LP: within 0.001No clear effect
Use Constrained CFR rather than the LP solverTransit game · Transit game, w=10computation time: more than an order of magnitudeHelped
Infer opponent strategies from partial observations using sequence-form constraintsConstrained CFR opponent modeling · Leduc Hold'emexpected value against target profileHelped
Use looser confidence intervals (higher confidence γ)Constrained CFR opponent modeling · Leduc Hold'emexpected value against target profile: fewer than 100 observed games on average; fewer than 200 observed games consistentlyHelped
Use lower confidence levels (tighter constraints)Constrained CFR opponent modeling · Leduc Hold'emexpected value against target profileMixed
Increase the number of observed games used to construct constraintsConstrained CFR opponent modeling · Leduc Hold'emexpected value against target profileHelped

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