Comparer des méthodes

	Apprentissage par renforcement faiblement supervisé	Apprentissage par renforcement
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s–present	1950s–1998
Auteur d'origine	Multiple contributors; reward-learning framing: Christiano et al. (2017)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Type	Reinforcement learning with imperfect or partial reward supervision	Sequential decision-making framework
Source fondatrice	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Alias	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL	RL, reward-based learning, trial-and-error learning, policy optimization
Apparentées	3	2
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.	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.