Linganisha mbinu

	Msisimko wa usaidizi wa kujifunza (Semi-supervised Reinforcement Learning)	Ujifunzaji wa Uimarishaji unaobadilika na Kanda
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020s	2009–2020
Mwanzilishi	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)
Aina	Semi-supervised training paradigm for RL agents	Transfer-based RL paradigm
Chanzo asilia	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.	Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170.
Majina mbadala	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning	Domain-Adaptive RL, DARL, Cross-domain RL, Transfer RL with domain adaptation
Zinazohusiana	6	2
Muhtasari	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.	Domain-Adaptive Reinforcement Learning (DARL) extends standard RL by enabling a policy trained in one environment or domain to transfer and generalise effectively to a different but related target domain. It addresses the domain-shift problem — where dynamics, observations, or reward structures differ between training and deployment — through alignment, adaptation, or domain-randomisation techniques, reducing the need to collect costly experience in the target domain.