Methoden vergelijken

	Zelfgesuperviseerd Reinforcement Learning	Reinforcement Learning
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2020	1950s–1998
Grondlegger	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Type	Self-supervised auxiliary-task learning for RL	Sequential decision-making framework
Oorspronkelijke bron	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
Aliassen	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL	RL, reward-based learning, trial-and-error learning, policy optimization
Verwant	4	2
Samenvatting	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.