Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Pembelajaran Penguatan Dalam (Deep Reinforcement Learning)× | Pembelajaran Pindahan× | |
|---|---|---|
| Bidang≠ | Pembelajaran Mendalam | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Pengasas≠ | Mnih, V. et al. (DQN) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Jenis≠ | Sequential decision-making (agent–environment interaction) | Learning paradigm |
| Sumber perintis≠ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Alias≠ | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Berkaitan≠ | 4 | 3 |
| Ringkasan≠ | Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return. | 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. |
| ScholarGateSet data ↗ |
|
|