Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak transfer beyond the training setting. A common response is to reuse information from previously trained models through transfer, distillation, ensemble methods, or federated training instead of learning each target task from random initialization. The literature on these mechanisms is fragmented, and published comparisons are hard to interpret because tasks, baselines, and compute budgets differ. This paper presents a PRISMA-guided systematic review of empirical studies on pretrained knowledge reuse in DRL. Starting from 589 records retrieved from IEEE Xplore, the ACM Digital Library, and citation tracing, we screened 570 unique records and assessed 89 full texts. After applying the final eligibility criteria, 15 empirical studies remained in the main synthesis. We analyzed them qualitatively across three factors: source-target similarity, diversity among reused models, and the fairness of comparisons against from-scratch baselines. Three patterns recur across the surviving corpus. First, positive results are concentrated in settings where source and target tasks share substantial structure or where the method includes an explicit gating or alignment mechanism. Second, evidence for ensembles and federated aggregation is promising but sparse and mostly limited to narrow settings. Third, compute-matched comparisons are rare, which weakens claims about efficiency gains over stronger single-agent baselines. The paper contributes a narrower and internally consistent review scope, a study-level synthesis of empirical evidence, and a provisional independence spectrum that should be treated as a hypothesis for future benchmarking rather than a validated metric.

翻译：深度强化学习（DRL）在雅达利（Atari）和围棋（Go）等领域取得了显著成果，但仍面临样本成本高、训练环境外迁移能力弱等挑战。一种常见应对策略是通过迁移学习、知识蒸馏、集成方法或联邦训练等方式，复用先前训练好的模型信息，而非从随机初始化开始学习每个目标任务。关于这些机制的文献较为零散，且由于任务、基线和计算预算的差异，已发表的对比结果难以解读。本文遵循PRISMA指南，对深度强化学习中预训练知识复用的实证研究进行了系统性综述。我们从IEEE Xplore、ACM数字图书馆及引文追踪检索到的589条记录出发，筛选出570条唯一记录并评估了89篇全文。在应用最终纳入标准后，共有15项实证研究进入主要综合环节。我们从三个维度对其进行定性分析：源-目标任务相似性、复用模型的多样性，以及与随机初始化基线对比的公平性。在纳入文献中，重复出现三种模式：第一，正面结果集中在源任务与目标任务共享显著结构，或方法包含显式门控/对齐机制的场景；第二，集成和联邦聚合的证据有前景但较稀少，且多局限于窄范围场景；第三，计算资源匹配的对比研究稀缺，削弱了关于相比强单智能体基线具有效率提升的论断。本文贡献了范围更窄但内部一致的综述框架、基于研究层面的实证证据综合，以及一个暂定的独立性谱系——该谱系应被视为未来基准测试的假设而非已验证的指标。