Monte Carlo Counterfactual Regret Minimization (MCCFR) has emerged as a cornerstone algorithm for solving extensive-form games, but its integration with deep neural networks introduces scale-dependent challenges that manifest differently across game complexities. This paper presents a comprehensive analysis of how neural MCCFR component effectiveness varies with game scale and proposes an adaptive framework for selective component deployment. We identify that theoretical risks such as nonstationary target distribution shifts, action support collapse, variance explosion, and warm-starting bias have scale-dependent manifestation patterns, requiring different mitigation strategies for small versus large games. Our proposed Robust Deep MCCFR framework incorporates target networks with delayed updates, uniform exploration mixing, variance-aware training objectives, and comprehensive diagnostic monitoring. Through systematic ablation studies on Kuhn and Leduc Poker, we demonstrate scale-dependent component effectiveness and identify critical component interactions. The best configuration achieves final exploitability of 0.0628 on Kuhn Poker, representing a 60% improvement over the classical framework (0.156). On the more complex Leduc Poker domain, selective component usage achieves exploitability of 0.2386, a 23.5% improvement over the classical framework (0.3703) and highlighting the importance of careful component selection over comprehensive mitigation. Our contributions include: (1) a formal theoretical analysis of risks in neural MCCFR, (2) a principled mitigation framework with convergence guarantees, (3) comprehensive multi-scale experimental validation revealing scale-dependent component interactions, and (4) practical guidelines for deployment in larger games.

翻译：蒙特卡洛反事实遗憾最小化（MCCFR）已成为求解扩展式博弈的基石算法，但其与深度神经网络的结合引入了随博弈规模变化的挑战，这些挑战在不同复杂度的博弈中表现各异。本文全面分析了神经MCCFR各组件效能如何随博弈规模变化，并提出了一种用于选择性部署组件的自适应框架。我们发现，诸如非平稳目标分布偏移、动作支撑集坍缩、方差爆炸及热启动偏差等理论风险具有规模依赖的表现模式，需要针对小型与大型博弈采用不同的缓解策略。我们提出的鲁棒深度MCCFR框架整合了延迟更新的目标网络、均匀探索混合、方差感知训练目标以及全面的诊断监控机制。通过在库恩扑克和勒杜克扑克上的系统消融实验，我们展示了组件效能的规模依赖性并识别出关键的组件交互作用。最优配置在库恩扑克上实现了0.0628的最终可剥削性，较经典框架（0.156）提升了60%。在更复杂的勒杜克扑克领域中，选择性使用组件实现了0.2386的可剥削性，较经典框架（0.3703）提升了23.5%，这凸显了精细组件选择相较于全面缓解策略的重要性。我们的贡献包括：（1）对神经MCCFR中风险的形式化理论分析；（2）具有收敛保证的原则性缓解框架；（3）揭示规模依赖性组件交互的全面多尺度实验验证；（4）面向更大规模博弈部署的实用指南。