方法对比
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| 域自适应强化学习× | 迁移学习× | |
|---|---|---|
| 领域≠ | 深度学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009–2020 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Transfer-based RL paradigm | Learning paradigm |
| 开创性文献≠ | Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | Domain-Adaptive RL, DARL, Cross-domain RL, Transfer RL with domain adaptation | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 2 | 3 |
| 摘要≠ | Domain-Adaptive Reinforcement Learning (DARL) extends standard RL by enabling a policy trained in one environment or domain to transfer and generalise effectively to a different but related target domain. It addresses the domain-shift problem — where dynamics, observations, or reward structures differ between training and deployment — through alignment, adaptation, or domain-randomisation techniques, reducing the need to collect costly experience in the target domain. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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