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| 준지도 강화학습× | 준지도 학습 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020s | 2018–2019 |
| 창시자≠ | Multiple contributors (Laskin, Srinivas, Abbeel et al.) | Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community |
| 유형≠ | Semi-supervised training paradigm for RL agents | Semi-supervised deep learning |
| 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 ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI ↗ |
| 별칭 | SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning | semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model |
| 관련≠ | 6 | 5 |
| 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. | Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance. |
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