Cooperative Strategy Evolution for Multi-Agent Systems via Post-Adversarial Fine-Tuning in Simulation Environments

The deployment of multi-agent systems in competitive domains such as cyber-defense, financial markets, and strategic wargaming necessitates robust and adaptive cooperative strategies. A key challenge lies in enabling agents to rapidly evolve their policies in response to discovered vulnerabilities or novel adversarial tactics encountered during operations. Simulation wargaming environments offer a controlled, cost-effective platform for extensive adversarial training and subsequent strategy refinement. However, effectively translating adversarial experiences into stable, generalized policy improvements remains an open problem. This paper proposes a novel cyclical framework for cooperative strategy evolution in multi-agent systems, centered on post-adversarial fine-tuning within simulation wargaming environments. The framework consists of three core phases: adversarial confrontation in a high-fidelity simulator, automated vulnerability assessment and trajectory sampling, and targeted policy fine-tuning using advanced multi-agent reinforcement learning (MARL) techniques. We formulate the problem as a meta-game where agents must adapt their cooperative policy based on outcomes from a population of adversarial strategies. The fine-tuning mechanism integrates a stabilized experience replay from critical adversarial episodes and employs a dynamics-aware policy gradient to ensure coherent multi-agent adaptation. Extensive experiments in diverse competitive simulation scenarios demonstrate that our framework enables agents to consistently close performance gaps exposed by adversaries, leading to evolved strategies with significantly enhanced robustness and win rates against adaptive opponents, compared to standard self-play or static training paradigms. The work provides a systematic, simulation-driven pathway for continual multi-agent strategy evolution.