Comparar métodos

	Aprendizaje por Refuerzo Semi-supervisado	Aprendizaje por Transferencia con Aprendizaje por Refuerzo
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2020s	2009 (survey); concept from early 2000s
Autor original	Multiple contributors (Laskin, Srinivas, Abbeel et al.)	Taylor, M. E. & Stone, P.
Tipo	Semi-supervised training paradigm for RL agents	Transfer learning paradigm for sequential decision-making
Fuente seminal	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.	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.
Alias	SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL
Relacionados	6	4
Resumen	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.	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.