Research map / Learning theory and algorithms / Submodular and game algorithms
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NES instead of a uniform joint baseline | CCE solving · 4 × 4 to 64 × 64 games | CCE Gap: 1.1006 -> 0.0274 (4 × 4); 1.0043 -> 0.0163 (8 × 8); 0.8861 -> 0.0173 (16 × 16); 0.7376 -> 0.0215 (32 × 32); 0.5864 -> 0. | Helped |
| Use NES instead of a uniform joint baseline | CCE solving · 4 × 4 to 16 × 16 games | Solver Gap: 0.3552 -> 0.0120 (4 × 4); 0.2513 -> 0.0054 (8 × 8); 0.2014 -> 0.0034 (16 × 16) | Helped |
| Apply an 8 × 8-trained NES zero-shot to other game sizes | NES · 16 × 16, 32 × 32, and 64 × 64 games | CCE Gap: 0.8861 -> 0.0000 (16 × 16); 0.7376 -> 0.0000 (32 × 32); 0.5864 -> 0.0000 (64 × 64) | Helped |
| Apply an 8 × 8-trained NES zero-shot to different game sizes | NES · 4 × 4 to 16 × 16 games | Solver Gap: 0.3552 -> 0.1500 (4 × 4); 0.2513 -> 0.0054 (8 × 8); 0.2014 -> 0.1089 (16 × 16) | Mixed |
| Replace the equivariant network with a linear network (ablation) | NES · MECCE 4 × 4 × 4 games | Solver Gap | Hurt |
| Remove invariant preprocessing and use naive payoff sampling (ablation) | NES · MECCE 4 × 4 × 4 games | Solver Gap | Hurt |
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