পদ্ধতির তুলনা করুন

	গভীর শক্তিশালীকরণ শিক্ষা	নিউরাল আর্কিটেকচার সার্চ
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015	2017
প্রবর্তক	Mnih, V. et al. (DQN)	Zoph, B. & Le, Q.V.
ধরন	Sequential decision-making (agent–environment interaction)	Automated architecture optimization (deep learning)
মৌলিক উৎস	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
অপর নাম	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
সম্পর্কিত	4	5
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.	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.