Data-Driven Knowledge Transfer in Batch $Q^*$ Learning

arXiv:2404.15209v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.lg data data-driven decision dynamic education environments explore feature healthcare knowledge making marketing spaces stat.me stat.ml transfer type