مقایسهٔ روش‌ها

	یادگیری تقویتی	شبکه عصبی بازگشتی
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	1950s–1998	1986–1990
پدیدآور	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)	Rumelhart, D. E.; Elman, J. L.
نوع	Sequential decision-making framework	Sequential neural network
منبع بنیادین	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
نام‌های دیگر	RL, reward-based learning, trial-and-error learning, policy optimization	RNN, Elman network, Jordan network, simple recurrent network
مرتبط	2	3
خلاصه	Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.