Comparer des méthodes

	Apprentissage par renforcement faiblement supervisé	Apprentissage par renforcement semi-supervisé
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s–present	2020s
Auteur d'origine	Multiple contributors; reward-learning framing: Christiano et al. (2017)	Multiple contributors (Laskin, Srinivas, Abbeel et al.)
Type	Reinforcement learning with imperfect or partial reward supervision	Semi-supervised training paradigm for RL agents
Source fondatrice	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	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.
Alias	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning
Apparentées	3	6
Résumé	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.	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.