Salīdzināt metodes

	Pusuzraudzītā pastiprinājuma mācīšanās	Pusuzraudzīts transformators
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020s	2018–2019
Autors	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community
Tips	Semi-supervised training paradigm for RL agents	Semi-supervised deep learning
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	5
Kopsavilkums	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.