So sánh phương pháp

	Học tăng cường sâu	Transfer Learning
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2015	2010 (formalized); 1990s (early roots)
Người khởi xướng	Mnih, V. et al. (DQN)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Loại	Sequential decision-making (agent–environment interaction)	Learning paradigm
Công trình gốc	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Tên gọi khác	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Liên quan	4	3
Tóm tắt	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.