Сравнение методов

	Адаптивное обучение с подкреплением в условиях смены домена	Перенос обучения
Область	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2009–2020	2010 (formalized); 1990s (early roots)
Автор метода	Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Transfer-based RL paradigm	Learning paradigm
Основополагающий источник	Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	Domain-Adaptive RL, DARL, Cross-domain RL, Transfer RL with domain adaptation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	2	3
Сводка	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.