Σύγκριση μεθόδων

	Ενισχυτική Μάθηση	Μέθοδοι Κλίσης Πολιτικής	Αναδρομικό Νευρωνικό Δίκτυο
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1950s–1998	1992	1986–1990
Δημιουργός	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)	Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)	Rumelhart, D. E.; Elman, J. L.
Τύπος	Sequential decision-making framework	Policy-based reinforcement learning	Sequential neural network
Θεμελιώδης πηγή	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Εναλλακτικές ονομασίες	RL, reward-based learning, trial-and-error learning, policy optimization	REINFORCE, actor-critic, policy optimization, politika gradyanı	RNN, Elman network, Jordan network, simple recurrent network
Συναφείς	2	4	3
Σύνοψη	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.	Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms.	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.