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| 준지도 강화학습× | 강화학습에서의 전이 학습× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020s | 2009 (survey); concept from early 2000s |
| 창시자≠ | Multiple contributors (Laskin, Srinivas, Abbeel et al.) | Taylor, M. E. & Stone, P. |
| 유형≠ | Semi-supervised training paradigm for RL agents | Transfer learning paradigm for sequential decision-making |
| 원전≠ | Zhan, X., Zhu, X., & Shi, H. (2022). Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4680–4688. link ↗ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ |
| 별칭 | SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL |
| 관련≠ | 6 | 4 |
| 요약≠ | Semi-supervised reinforcement learning (SSRL) combines standard reinforcement learning — where an agent learns from sparse reward signals — with semi-supervised techniques that extract structure from unlabeled environment interactions. The goal is to improve sample efficiency and generalization when reward feedback is costly, delayed, or available only for a fraction of the agent's experience. | 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. |
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