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| Pembelajaran Penguatan Dalam× | XGBoost× | |
|---|---|---|
| Bidang≠ | Pembelajaran Mendalam | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2015 | 2016 |
| Pencetus≠ | Mnih, V. et al. (DQN) | Chen, T. & Guestrin, C. |
| Tipe≠ | Sequential decision-making (agent–environment interaction) | Ensemble (gradient-boosted decision trees) |
| Sumber perintis≠ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL | XGBoost, extreme gradient boosting, scalable tree boosting |
| Terkait≠ | 4 | 5 |
| 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateSet data ↗ |
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