השוואת שיטות

	למידת חיזוק עמוקה	Transfer Learning
תחום	למידה עמוקה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2015	2010 (formalized); 1990s (early roots)
הוגה השיטה	Mnih, V. et al. (DQN)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
סוג	Sequential decision-making (agent–environment interaction)	Learning paradigm
מקור מכונן	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.
כינויים	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	TL, domain adaptation, fine-tuning, pre-trained model adaptation
קשורות	4	3
תקציר	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.