Comparer des méthodes

	Apprentissage par transfert avec apprentissage par renforcement	Apprentissage par renforcement
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2009 (survey); concept from early 2000s	1950s–1998
Auteur d'origine	Taylor, M. E. & Stone, P.	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Type	Transfer learning paradigm for sequential decision-making	Sequential decision-making framework
Source fondatrice	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Alias	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL	RL, reward-based learning, trial-and-error learning, policy optimization
Apparentées	4	2
Résumé	Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.	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.