方法对比
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| 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× | 强化学习× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009 (survey); concept from early 2000s | 1950s–1998 |
| 提出者≠ | Taylor, M. E. & Stone, P. | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| 类型≠ | Transfer learning paradigm for sequential decision-making | Sequential decision-making framework |
| 开创性文献≠ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| 别名 | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL | RL, reward-based learning, trial-and-error learning, policy optimization |
| 相关≠ | 4 | 2 |
| 摘要≠ | 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. | Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback. |
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