Linganisha mbinu

	Ujifunzaji wa Uimarishaji Unaojisimamia Kwenyewe	Kujifunza kwa Kuhamisha kwa Kutumia Kujifunza kwa Uimarishaji
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020	2009 (survey); concept from early 2000s
Mwanzilishi	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)	Taylor, M. E. & Stone, P.
Aina	Self-supervised auxiliary-task learning for RL	Transfer learning paradigm for sequential decision-making
Chanzo asilia	Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650.	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.
Majina mbadala	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL
Zinazohusiana	4	4
Muhtasari	Self-supervised Reinforcement Learning (SSL-RL) augments standard RL training with self-supervised auxiliary objectives — such as contrastive, predictive, or data-augmentation-based tasks — applied to the agent's own experience. These objectives improve the quality of learned representations without requiring extra human labels, enabling faster convergence and better sample efficiency, especially in high-dimensional observation spaces like raw pixels.	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.