Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kujifunza kwa Kuhamisha kwa Kutumia Kujifunza kwa Uimarishaji× | Ujifunzaji wa Kuimarisha Uliosafishwa (Fine-Tuned Reinforcement Learning)× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2009 (survey); concept from early 2000s | 2017–2022 |
| Mwanzilishi≠ | Taylor, M. E. & Stone, P. | Christiano, P. et al.; Ouyang, L. et al. |
| Aina≠ | Transfer learning paradigm for sequential decision-making | Policy adaptation via fine-tuning |
| Chanzo asilia≠ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ | 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 ↗ |
| Majina mbadala | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL | RL fine-tuning, policy fine-tuning, RLHF, reinforcement learning from human feedback |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
|
|