Сравнение методов

	Обучение с переносом и обучение с подкреплением	Дообученное обучение с подкреплением
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2009 (survey); concept from early 2000s	2017–2022
Автор метода	Taylor, M. E. & Stone, P.	Christiano, P. et al.; Ouyang, L. et al.
Тип	Transfer learning paradigm for sequential decision-making	Policy adaptation via fine-tuning
Основополагающий источник	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.	Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
Другие названия	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL	RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback
Связанные	4	5
Сводка	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.	Fine-Tuned Reinforcement Learning adapts a pre-trained policy or model to a new task or behavioral objective using reinforcement signals — including human feedback — rather than retraining from scratch. Popularized by RLHF, it is the core technique behind aligning large language models and adapting deep RL agents to specialized environments with minimal additional data.