Sammenlign metoder

	Svagt Overvåget Forstærkningslæring	Selvovervåget forstærkningslæring
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2010s–present	2020
Ophavsperson	Multiple contributors; reward-learning framing: Christiano et al. (2017)	Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)
Type	Reinforcement learning with imperfect or partial reward supervision	Self-supervised auxiliary-task learning for RL
Oprindelig kilde	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.
Aliasser	WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL	SSL-RL, self-supervised RL, representation-based reinforcement learning, auxiliary-task RL
Relaterede	3	4
Resumé	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.