পদ্ধতির তুলনা করুন

	গভীর শক্তিশালীকরণ শিক্ষা	ট্রান্সফার লার্নিং
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015	2010 (formalized); 1990s (early roots)
প্রবর্তক	Mnih, V. et al. (DQN)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Sequential decision-making (agent–environment interaction)	Learning paradigm
মৌলিক উৎস	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.
অপর নাম	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	4	3
সারসংক্ষেপ	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.