Resilience Metric and Enhancement for Adversarial Operational Loop Networks via Reinforcement Learning

Resilience optimization in adversarial Multi-Agent Mission Systems (MAMSs) requires more than resilience measurement: it requires a decision mechanism whose objective is explicitly aligned with the resilience metric itself. This paper proposes a net-effectiveness-based evaluation–optimization framework for bilateral temporal adversarial operational-loop networks (BTAR), turning resilience evaluation into a reusable world model and reward source for decision-making. We model bilateral confrontation as a heterogeneous temporal network and quantify resilience through the integral of net effectiveness, which captures temporal and adversarial dynamics beyond static topological indicators. We then formulate resilience enhancement as a scale-invariant, cost-aware Markov decision process and solve it with a hierarchically decoupled reinforcement learning architecture: an upper-level PPO policy learns when and for which chains to trigger selective replanning, while a lower-level greedy routine selects concrete replacement nodes. In this way, the optimization objective remains formally aligned with the evaluation metric. Experiments on a reproducible multi-scale, multi-style scenario pool show that the learned policy improves Rtotal from 0.3673 to 1.1696, reaching 96.8% of an idealized oracle benchmark (1.2082), while slightly outperforming the always-replan rule (1.1482) with a lower trigger rate. Under nonzero replanning cost, retraining yields a cost-aware policy whose trigger rate drops to 21.2% and whose cost-adjusted resilience at the target cost setting increases by +0.15, surpassing the greedy oracle benchmark in the high-cost regime. These results show that coupling net-effectiveness evaluation with hierarchical RL yields a deployable, scale-invariant, and cost-aware resilience optimizer.