In data-driven decision-making in marketing, healthcare, and education, it is
desirable to utilize a large amount of data from existing ventures to navigate
high-dimensional feature spaces and address data scarcity in new ventures. We
explore knowledge transfer in dynamic decision-making by concentrating on batch
stationary environments and formally defining task discrepancies through the
lens of Markov decision processes (MDPs). We propose a framework of Transferred
Fitted $Q$-Iteration algorithm with general function approximation, enabling
the direct estimation of the optimal action-state function $Q^*$ using both
target and source data. We establish the relationship between statistical
performance and MDP task discrepancy under sieve approximation, shedding light
on the impact of source and target sample sizes and task discrepancy on the
effectiveness of knowledge transfer. We show that the final learning error of
the $Q^*$ function is significantly improved from the single task rate both
theoretically and empirically.

在数据驱动的决策制定中，通过利用现有企业的大量数据来导航高维特征空间，解决新企业中数据稀缺问题，在动态决策制定中探讨知识转移，并通过马尔可夫决策过程的角度形式定义任务差异，提出了具有通用函数逼近的转移拟合 Q - 迭代算法框架，可直接估计目标和源数据下的最优动作状态函数 Q*，在筛选逼近下，阐明了统计性能与 MDP 任务差异之间的关系，揭示了知识转移的有效性受源样本数量、目标样本数量和任务差异的影响，并从理论和实证上显示，Q * 函数的最终学习误差显著提高于单一任务速率。