Bandingkan kaedah

	Pembelajaran Pengukuhan Separa Seliaan	Pembelajaran Pengukuhan
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2020s	1950s–1998
Pengasas	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Jenis	Semi-supervised training paradigm for RL agents	Sequential decision-making framework
Sumber perintis	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.	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Alias	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning	RL, reward-based learning, trial-and-error learning, policy optimization
Berkaitan	6	2
Ringkasan	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.	Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.