Linganisha mbinu

	Jifunze la Uimarishaji la Usimamizi dhaifu	Ujifunzaji wa Uimarishaji Unaojisimamia Kwenyewe
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2010s–present	2020
Mwanzilishi	Multiple contributors; reward-learning framing: Christiano et al. (2017)	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)
Aina	Reinforcement learning with imperfect or partial reward supervision	Self-supervised auxiliary-task learning for RL
Chanzo asilia	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	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.
Majina mbadala	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL
Zinazohusiana	3	4
Muhtasari	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.	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.