Research map / Multi-agent systems and games / Games and equilibria
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use maximum Gini correlated equilibrium (MGCCE) as the JPSRO meta-solver instead of uniform, alpha-Rank, PRD, or random solvers | JPSRO(CCE) meta-solver · Kuhn Poker (3-player) | CCE Gap Sum (under MWCCE) | Helped |
| Use a CCE meta-solver (CCE-based MS variants) in JPSRO instead of traditional PSRO meta-solvers | JPSRO on Trade Comm · Trade Comm (3-item, common payoff) | Value sum (under MWCCE) | Helped |
| Use the approximate CCE meta-solver (approx-eps MGCCE) in JPSRO (ablation) | JPSRO on Trade Comm · Trade Comm (3-item, common payoff) | convergence to optimal value sum | Hurt |
| Use JPSRO with a CCE meta-solver on Sheriff | JPSRO on Sheriff · Sheriff (2-player general-sum) | value sum (under MWCCE) | Helped |
| Use random-vertex CCE meta-solver (RVCCE) for exploration in JPSRO | JPSRO · general | qualitative exploration/value | Helped |
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