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	Ewolucja Różnicowa	Głębokie uczenie ze wzmocnieniem
Dziedzina	Optymalizacja	Uczenie głębokie
Rodzina	Process / pipeline	Machine learning
Rok powstania	1997	2015
Twórca	Rainer Storn & Kenneth Price	Mnih, V. et al. (DQN)
Typ	Population-based stochastic metaheuristic	Sequential decision-making (agent–environment interaction)
Źródło pierwotne	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.
Inne nazwy	DE algorithm, Diferansiyel Evrim (DE), DE optimization	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL
Pokrewne	5	4
Podsumowanie	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.