研究多智能体环境中智能体持续学习时的性能退化问题，提出可预测失败的关键结构

In a stationary decentralized Markov game, learning peers generate an episode-indexed sequence of induced MDPs for any focal agent. The joint game remains stationary while the focal agent's rewards and dynamics drift, forming an agent-centric continual reinforcement-learning problem. Marginalizing peers whose policies are fixed within an episode preserves every focal trajectory law and expected return. Success-conditioned reusable structure may therefore degrade under peer updates. An \emph{invariant core} represents such structure through maximal abstract patterns appearing in a high fraction of successful focal trajectories. The main result is a worst-case-tight conditioning theorem: trajectory-law drift $\varepsilon$ can reduce a candidate's success-conditioned coverage by at most $\frac{\varepsilon}{p_0}$, where $p_0$ is its reference success mass, and the coefficient is sharp. Peer-policy movement supplies $\varepsilon$; positive coverage margin then yields a certified $Ω(\frac{1}η)$ survival horizon and, under an explicit effective-conflict condition realized by exact policy gradient in an analytic class, a matching $Θ(\frac{1}η)$ first-exit law. With calibrated success mass and executability, the same certificate yields policy-value, library-selection, and transfer-regret guarantees. An exactly solvable corridor confirms the structural predictions, including the inverse-rate lifetime ($R^2>0.9999$). Two registered 64-stream studies in continual control and cue-MNIST show that core erosion predicts impending failure and enables near-oracle intervention; an exploratory reanalysis of eight learned-partner Level-Based Foraging development pairings suggests the same erosion--failure link under peer learning.