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Semi-supervised Reinforcement Learning

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.

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Sources

  1. 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 ↗
  2. 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. link ↗

Related methods

Domain-adaptive reinforcement learningReinforcement LearningSelf-supervised Reinforcement LearningSemi-supervised TransformerTransfer Learning with Reinforcement LearningWeakly supervised reinforcement learning

Referenced by

Self-supervised Reinforcement LearningWeakly supervised reinforcement learning

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ScholarGate — Semi-supervised Reinforcement Learning (Semi-supervised Reinforcement Learning (SSRL)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/semi-supervised-reinforcement-learning