Overview
PlatinCFR refines traditional Counterfactual Regret Minimization (CFR) techniques, achieving approximately 50% faster convergence compared to MCCFR. Our method employs adaptive regret updates and optimized convergence criteria to reduce the number of iterations needed to reach low exploitability.
Key Findings
- Adaptive Updates: Dynamic adjustment of update parameters accelerates convergence.
- Optimized Criteria: Improved thresholds enable early reinforcement of effective strategies.
- Empirical Results: Benchmark tests show a 50% reduction in convergence time without compromising solution quality.
Conclusion
PlatinCFR offers a practical enhancement for large-scale, real-time decision-making applications in strategic domains. We continue to refine our approach and invite further collaboration and validation.
PlatinAI 