Compară metode

	Învățare prin consolidare semi-supervizată	Învățare prin consolidare slab supervizată
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2020s	2010s–present
Autorul original	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Multiple contributors; reward-learning framing: Christiano et al. (2017)
Tip	Semi-supervised training paradigm for RL agents	Reinforcement learning with imperfect or partial reward supervision
Sursa seminală	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
Denumiri alternative	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL
Înrudite	6	3
Rezumat	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.	Weakly supervised reinforcement learning (WSRL) trains agents in environments where the reward signal is imperfect, sparse, delayed, or only partially informative — unlike dense fully-supervised RL. The agent must learn effective policies despite incomplete feedback, using auxiliary signals, reward modeling, or preference learning to compensate for the weak supervision.