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	Samostalno nadgledano pojačano učenje	Samonadzirana konvoluciona neuronska mreža
Oblast	Duboko učenje	Duboko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2020	2018–2020
Tvorac	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)	LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)
Tip	Self-supervised auxiliary-task learning for RL	Self-supervised deep learning
Temeljni izvor	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.	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.
Drugi nazivi	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL	Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN
Srodne	4	5
Sažetak	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.	A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures.