So sánh phương pháp

	Học chuyển giao với Học tăng cường	Học tăng cường thích ứng miền
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2009 (survey); concept from early 2000s	2009–2020
Người khởi xướng	Taylor, M. E. & Stone, P.	Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)
Loại	Transfer learning paradigm for sequential decision-making	Transfer-based RL paradigm
Công trình gốc	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.	Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170.
Tên gọi khác	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL	Domain-Adaptive RL, DARL, Cross-domain RL, Transfer RL with domain adaptation
Liên quan	4	2
Tóm tắt	Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.	Domain-Adaptive Reinforcement Learning (DARL) extends standard RL by enabling a policy trained in one environment or domain to transfer and generalise effectively to a different but related target domain. It addresses the domain-shift problem — where dynamics, observations, or reward structures differ between training and deployment — through alignment, adaptation, or domain-randomisation techniques, reducing the need to collect costly experience in the target domain.