مقایسهٔ روش‌ها

	یادگیری تقویتی نیمه‌نظارت‌شده	یادگیری تقویتی
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2020s	1950s–1998
پدیدآور	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
نوع	Semi-supervised training paradigm for RL agents	Sequential decision-making framework
منبع بنیادین	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
نام‌های دیگر	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning	RL, reward-based learning, trial-and-error learning, policy optimization
مرتبط	6	2
خلاصه	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.