Machine learningDeep learning / NLP / CV
弱监督强化学习
弱监督强化学习（WSRL）在奖励信号不完美、稀疏、延迟或仅部分信息的环境中训练智能体——这与密集的全监督强化学习不同。智能体必须在信息不完整的反馈下学习有效的策略，利用辅助信号、奖励建模或偏好学习来弥补弱监督。
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来源
- Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
- Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S. & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems (NeurIPS), 30. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Reinforcement Learning. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-reinforcement-learning
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