เปรียบเทียบวิธี

	การเรียนรู้แบบเสริมกำลังเชิงลึก	โครงข่ายประสาทเทียมแบบเวียนซ้ำ (Recurrent Neural Network - RNN)
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2015	1986–1990
ผู้ริเริ่ม	Mnih, V. et al. (DQN)	Rumelhart, D. E.; Elman, J. L.
ประเภท	Sequential decision-making (agent–environment interaction)	Sequential neural network
แหล่งต้นตำรับ	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
ชื่อเรียกอื่น	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	RNN, Elman network, Jordan network, simple recurrent network
ที่เกี่ยวข้อง	4	3
สรุป	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.