Research map / Multi-agent systems and games / Games and equilibria
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| run ICFR (internal regret minimizer per infoset plus external minimizers) in multi-player games with chance | ICFR · Kuhn, Leduc, Goofspiel, Battleship instances | EFCE gap (max deviation) | Helped |
| use ICFR instead of prior algorithms for approximate NFCCE on large Leduc | equilibrium computation · Leduc poker, 0.1-NFCCE | time to reach epsilon 0.1: 24h (not reached, 1200 infosets) -> ~9h (>9k infosets) | Helped |
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