Research map / Bandits and online learning / Online convex optimization and games
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use OMD with Euclidean regularization and learning rate η = 0.1 instead of the Nash equilibrium outcome | 3×3 bimatrix game · 3×3 bimatrix game | social welfare: 0.5833 -> 0.9819 | Helped |
| Run OMD with Euclidean regularization for 1000 iterations | 3×3 bimatrix game · 3×3 bimatrix game | strong CCE parameter: 0.1525 | Helped |
| Run OMD for 5000 iterations | OMD · Goofspiel | strong CCE parameter: 0.0637 | Helped |
| Use OMD in the zero-sum instance rather than a general-sum instance | OMD · Liar's Dice | last-iterate Nash gap | Helped |
| Continue OMD dynamics beyond the initial apparent approach toward Nash equilibrium | OMD · Goofspiel | average-distribution CCE gap: 600 | Helped |
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