विधियों की तुलना करें

	डीप रीइन्फोर्समेंट लर्निंग	न्यूरल आर्किटेक्चर सर्च
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2015	2017
प्रवर्तक	Mnih, V. et al. (DQN)	Zoph, B. & Le, Q.V.
प्रकार	Sequential decision-making (agent–environment interaction)	Automated architecture optimization (deep learning)
मौलिक स्रोत	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.
उपनाम	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
संबंधित	4	5
सारांश	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.