방법 비교

	강화학습에서의 전이 학습	도메인 적응 강화학습
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2009 (survey); concept from early 2000s	2009–2020
창시자	Taylor, M. E. & Stone, P.	Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)
유형	Transfer learning paradigm for sequential decision-making	Transfer-based RL paradigm
원전	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.
별칭	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
관련	4	2
요약	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.