Linganisha mbinu

	Ujifunzaji wa Uimarishaji Unaojisimamia Kwenyewe	Jifunze kwa Kuimarisha (Reinforcement Learning)
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020	1950s–1998
Mwanzilishi	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Aina	Self-supervised auxiliary-task learning for RL	Sequential decision-making framework
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.	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Majina mbadala	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL	RL, reward-based learning, trial-and-error learning, policy optimization
Zinazohusiana	4	2
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.	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.