Research map / Learning theory and algorithms / Submodular and game algorithms
Safe Subgame Resolving for Extensive Form Correlated Equilibrium
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply SW-maximizing safe subgame resolving to the Uniform blueprint | Uniform blueprint · Battleship n=6, T=3, J=36, γ=2 | social welfare (×1e-2): -1.23 -> -.772 | Helped |
| Apply SW-maximizing safe subgame resolving to the Uniform blueprint | Uniform blueprint · Battleship n=6, T=3, J=36, γ=5 | social welfare (×1e-2): -4.94 -> -2.47 | Helped |
| Apply SW-maximizing safe subgame resolving to the Jittered blueprint | Jittered blueprint · Battleship n=6, T=3, J=36, γ=2 | social welfare (×1e-2): -1.25 -> -.627 | Helped |
| Apply SW-maximizing safe subgame resolving to the Jittered blueprint | Jittered blueprint · Battleship n=6, T=3, J=36, γ=5 | social welfare (×1e-2): -4.99 -> -1.95 | Helped |
| Apply SW-maximizing safe subgame resolving | Uniform and Jittered blueprints · Battleship n=3, T=2, J=9, γ=2 and γ=5 | social welfare (×1e-2): -3.70 -> -3.70; -14.8 -> -14.8; -3.55 -> -3.55; -14.2 -> -14.2 | No clear effect |
| Use regret minimization for online refinement rather than storing a full correlation plan | Regret-minimization refinement · Large Battleship instance; T'=1, γ=2, Uniform blueprint | resolving time per iteration: less than 0.5 seconds | Helped |
| Use regret minimization for online refinement rather than storing a full correlation plan | Regret-minimization refinement · Large Battleship instance; T'=1, γ=2, Uniform blueprint | memory usage: no more than 2GB | Helped |
Closest papers by meaning
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