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| Semi-supervidiertes Reinforcement Learning× | Reinforcement Learning× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2020s | 1950s–1998 |
| Urheber≠ | Multiple contributors (Laskin, Srinivas, Abbeel et al.) | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| Typ≠ | Semi-supervised training paradigm for RL agents | Sequential decision-making framework |
| Wegweisende Quelle≠ | Zhan, X., Zhu, X., & Shi, H. (2022). Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4680–4688. link ↗ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| Aliasnamen | SSRL, semi-supervised RL, RL with unlabeled data, label-efficient reinforcement learning | RL, reward-based learning, trial-and-error learning, policy optimization |
| Verwandt≠ | 6 | 2 |
| Zusammenfassung≠ | Semi-supervised reinforcement learning (SSRL) combines standard reinforcement learning — where an agent learns from sparse reward signals — with semi-supervised techniques that extract structure from unlabeled environment interactions. The goal is to improve sample efficiency and generalization when reward feedback is costly, delayed, or available only for a fraction of the agent's experience. | 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. |
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