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此外，CFR is an iterative method that breaks down regret minimization over different information states. Each round, it collects "counterfactual regret"—the potential gain from alternative actions—and forms a new strategy based on accumulated positive regret. Over repeated cycles, the average approach approaches a Nash Equilibrium. Manual adjustments led to variants like DCFR and PCFR+, which enhance convergence through discounting or predictive updates.
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