Adaptive Fusion-based Robustness Evaluation Method for Multi-Agent Game Strategies

LI Junwei; RUAN Shulan; LIANG Jiaxuan; LIU Yu; HE You

doi:10.12263/DZXB.20251264

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Adaptive Fusion-based Robustness Evaluation Method for Multi-Agent Game Strategies

更新时间：2026-05-06

- Adaptive Fusion-based Robustness Evaluation Method for Multi-Agent Game Strategies
- ACTA ELECTRONICA SINICA Pages: 1-15(2026)
- 作者机构：
  
  1.清华大学深圳国际研究生院，广东深圳 518055
  2.哈尔滨工业大学（深圳）计算机科学与技术学院，广东深圳 518055
  3.清华大学电子工程系，北京 100084
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62293544;62425117;62506205);China Postdoctoral Science Foundation(2025T180426);Postdoctoral Fellowship Program of CPSF(GZB20250393)
- DOI：10.12263/DZXB.20251264
  CLC： TP181;
- Received：16 February 2026，
  
  Accepted：19 March 2026，
  
  Online First：06 May 2026，
- 稿件说明：
移动端阅览
LI Junwei, RUAN Shulan, LIANG Jiaxuan, et al. Adaptive Fusion-based Robustness Evaluation Method for Multi-Agent Game Strategies[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-15.
DOI：

LI Junwei, RUAN Shulan, LIANG Jiaxuan, et al. Adaptive Fusion-based Robustness Evaluation Method for Multi-Agent Game Strategies[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-15. DOI： 10.12263/DZXB.20251264.

摘要

随着多智能体强化学习算法的快速发展，智能体在博弈任务中的协作与竞争能力得到了显著提升。然而，面对实际场景中环境的动态变化，智能体策略在跨环境迁移中的性能波动问题日益凸显。尽管当前已涌现出对抗训练、域随机化等鲁棒性增强技术，但现有的鲁棒性评估体系仍存在明显局限。现有方法往往仅关注平均奖励等单一性能指标的变化，忽视了碰撞次数等反映安全性或稳定性的特征，难以全面衡量策略的稳定性。此外，由于缺乏统一的测试基准，不同研究常依赖特定的实验环境参数设定，导致算法难以在不同的场景条件下进行公平的横向比较。这些局限制约了博弈策略的实际落地与迭代优化。为此，本文提出了面向多智能体博弈策略的多维自适应融合鲁棒性评估方法，旨在通过数学形式化建模实现对策略稳定性的量化分析。首先，本文设计了基于条件变异系数（Conditional Coefficient of Variation， CondCV）的鲁棒性评分指标（Robustness Score， RS），用于精确捕捉并融合多种基础评测指标在环境扰动下的波动特征。通过消除指标间的量纲差异，该方法构建了一种标准化的通用度量，具备良好的自适应性与评估公平性，广泛适用于多智能体协作、对抗等各类环境下的策略评估。同时，针对多维指标权重的分配，本文提出基于α-Rank演化博弈的权重自适应融合框架。该框架将指标间的排序一致性建模为博弈过程，通过计算稳态分布获得客观权重，并与先验权重进行动态融合，有效平衡了指标的客观稳定性与专家先验知识。为验证方法的有效性，本文基于Isaac Sim平台自主构建了高度可配置的实验环境，涵盖对抗与协作两类典型的多智能体博弈场景，并集成多种主流算法开展了系统性的实验验证。实验结果表明，该评估方法可有效度量策略在不同环境设定下的稳定性，具备多维波动捕捉能力和跨任务通用性，为算法优化与评估提供了理论支持和参考。最后，本文探讨了评估方法在虚实迁移中的应用潜力，并提出了相应的可行方案，为未来研究提供了参考。

Abstract

With the rapid development of multi-agent reinforcement learning algorithms

agents’ capabilities for cooperation and competition in game-based tasks have significantly improved. However

given the dynamic changes of real-world environments

performance fluctuations of strategies during cross-environment transfer have become increasingly prominent. Although robustness enhancement techniques such as adversarial training and domain randomization have emerged

existing robustness evaluation frameworks still exhibit evident limitations. Current methods often focus only on changes in a single performance metric such as average reward

while neglecting safety or stability metrics such as collision frequency

making it difficult to comprehensively evaluate strategy stability. In addition

the lack of unified evaluation benchmarks leads different studies to rely on specific experimental parameter settings

hindering fair comparisons across diverse scenarios. These limitations restrict the practical deployment and iterative optimization of game strategies. To address these issues

we propose a robustness evaluation method for multi-agent game strategies via multidimensional adaptive fusion

aiming to provide a quantitative analysis of strategy stability through mathematically formalized modeling. First

we design a robustness score (RS) based on the conditional coefficient of variation (CondCV) to accurately capture and fuse the fluctuation characteristics of base metrics under environmental perturbations. By eliminating dimensional differences among metrics

the method establishes a standardized and generalizable measurement with strong adaptability and evaluation fairness

making it broadly applicable to strategy evaluation in cooperative

competitive

and other multi-agent environments. To address the weight assignment for multidimensional metrics

we propose an adaptive weight fusion framework based on an adversarial α-Rank evolutionary game. This framework models ranking consistency among metrics as a game process

derives objective weights from the stationary distribution

and dynamically fuses them with expert prior weights

achieving a balance between objective metric stability and expert prior knowledge. To validate the effectiveness of our method

we developed highly configurable multi-agent environments based on Isaac Sim that cover typical adversarial and cooperative game scenarios

and conducted systematic experiments with various mainstream algorithms. Experimental results demonstrate that the evaluation method can effectively measure strategy stability under diverse environmental settings

exhibiting multidimensional fluctuation-capturing capability and cross-task generality

thereby providing theoretical support and reference for algorithm optimization and evaluation. Finally

we discuss the potential application of the evaluation method in sim-to-real transfer and propose corresponding feasible solutions

offering insights for future research.

关键词

Keywords

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