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	Apprendimento per Rinforzo Semi-Supervisionato	Apprendimento per rinforzo
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2020s	1950s–1998
Ideatore	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Tipo	Semi-supervised training paradigm for RL agents	Sequential decision-making framework
Fonte seminale	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
Correlati	6	2
Sintesi	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.