方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 微调强化学习× | 迁移学习与强化学习 (Transfer RL) 是一种训练范式，其中代理在一个或多个源任务中获得的知识× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2022 | 2009 (survey); concept from early 2000s |
| 提出者≠ | Christiano, P. et al.; Ouyang, L. et al. | Taylor, M. E. & Stone, P. |
| 类型≠ | Policy adaptation via fine-tuning | Transfer learning paradigm for sequential decision-making |
| 开创性文献≠ | Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744. link ↗ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ |
| 别名 | RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL |
| 相关≠ | 5 | 4 |
| 摘要≠ | Fine-Tuned Reinforcement Learning adapts a pre-trained policy or model to a new task or behavioral objective using reinforcement signals — including human feedback — rather than retraining from scratch. Popularized by RLHF, it is the core technique behind aligning large language models and adapting deep RL agents to specialized environments with minimal additional data. | Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments. |
| ScholarGate数据集 ↗ |
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