विधियों की तुलना करें

	डीप रीइन्फोर्समेंट लर्निंग	Dynamic Programming
क्षेत्र	गहन अधिगम	अनुकूलन
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	2015	1957
प्रवर्तक	Mnih, V. et al. (DQN)	Richard Bellman
प्रकार	Sequential decision-making (agent–environment interaction)	Exact combinatorial optimization via recursive decomposition
मौलिक स्रोत	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
उपनाम	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama
संबंधित	4	3
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.	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.