เปรียบเทียบวิธี

	การเรียนรู้แบบเสริมกำลังเชิงลึก	การโปรแกรมเชิงพลวัต
สาขาวิชา	การเรียนรู้เชิงลึก	การหาค่าเหมาะที่สุด
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2015	1957
ผู้ริเริ่ม	Mnih, V. et al. (DQN)	Richard Bellman
ประเภท	Sequential decision-making (agent–environment interaction)	Exact combinatorial optimization via recursive decomposition
แหล่งต้นตำรับ	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6
ชื่อเรียกอื่น	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama
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.	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.