Σύγκριση μεθόδων

	Δυναμικός Προγραμματισμός	Βαθιά Ενισχυτική Μάθηση
Πεδίο	Βελτιστοποίηση	Βαθιά Μάθηση
Οικογένεια	Process / pipeline	Machine learning
Έτος προέλευσης	1957	2015
Δημιουργός	Richard Bellman	Mnih, V. et al. (DQN)
Τύπος	Exact combinatorial optimization via recursive decomposition	Sequential decision-making (agent–environment interaction)
Θεμελιώδης πηγή	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.
Εναλλακτικές ονομασίες	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL
Συναφείς	3	4
Σύνοψη	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.	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.