Research map / Learning theory and algorithms / Submodular and game algorithms
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use exponentially decaying regularization, targeting η=0.05 | FoReL · Leduc Poker | NashConv: 0.12 -> 0.10 | Helped |
| Use very low regularization (ablation) | FoReL with decaying regularization · Leduc Poker | NashConv | Hurt |
| Periodically adapt the reward using the previous policy as the regularization reference | FoReL · Leduc Poker | convergence | Helped |
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