Comparer des méthodes

	Apprentissage par renforcement semi-supervisé	Apprentissage par renforcement faiblement supervisé
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2020s	2010s–present
Auteur d'origine	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Multiple contributors; reward-learning framing: Christiano et al. (2017)
Type	Semi-supervised training paradigm for RL agents	Reinforcement learning with imperfect or partial reward supervision
Source fondatrice	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	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL
Apparentées	6	3
Résumé	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.