Comparer des méthodes

	Apprentissage par renforcement profond	Programmation en nombres entiers
Domaine	Apprentissage profond	Optimisation
Famille	Machine learning	Process / pipeline
Année d'origine	2015	1958
Auteur d'origine	Mnih, V. et al. (DQN)	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
Type	Sequential decision-making (agent–environment interaction)	Mathematical optimisation — exact combinatorial method
Source fondatrice	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669
Alias	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL	IP, MIP, mixed-integer programming, mixed-integer linear programming
Apparentées	4	4
Résumé	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.	Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.