Salīdzināt metodes

	Pašuzraudzītā pastiprinātā mācīšanās	Pārneses mācīšanās ar pastiprinājuma mācīšanos
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020	2009 (survey); concept from early 2000s
Autors	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)	Taylor, M. E. & Stone, P.
Tips	Self-supervised auxiliary-task learning for RL	Transfer learning paradigm for sequential decision-making
Pirmavots	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.
Citi nosaukumi	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
Saistītās	4	4
Kopsavilkums	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.