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自监督强化学习

自监督强化学习（SSL-RL）通过对智能体自身经验应用自监督辅助目标（例如对比、预测或基于数据增强的任务），来增强标准强化学习训练。这些目标无需额外的人工标签即可提高学习表征的质量，从而实现更快的收敛和更好的样本效率，尤其是在原始像素等高维观测空间中。

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自监督强化学习
强化学习自监督卷积神经网络半监督强化学习迁移学习与强化学习 (Transfer RL)…微调强化学习多模态强化学习弱监督强化学习

来源

  1. 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 ↗
  2. Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2021). Reinforcement Learning with Augmented Data. Advances in Neural Information Processing Systems (NeurIPS), 33, 19884–19895. link ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Reinforcement Learning (SSL-augmented RL). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-reinforcement-learning

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被引用于

微调强化学习多模态强化学习半监督强化学习弱监督强化学习

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ScholarGate — Self-supervised Reinforcement Learning (Self-supervised Reinforcement Learning (SSL-augmented RL)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/self-supervised-reinforcement-learning · 数据集： https://doi.org/10.5281/zenodo.20539026