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	Wzmocnione uczenie ze sprzężeniem zwrotnym z częściowym nadzorem	Transformery z uczeniem półnadzorowanym
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2020s	2018–2019
Twórca	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community
Typ	Semi-supervised training paradigm for RL agents	Semi-supervised deep learning
Źródło pierwotne	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.	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.
Inne nazwy	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
Pokrewne	6	5
Podsumowanie	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.