Methoden vergleichen

	Policy-Gradient-Methoden	Stochastischer Gradientenabstieg (SGD)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1992	1951
Urheber	Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)	Robbins, H. & Monro, S.
Typ	Policy-based reinforcement learning	First-order iterative optimization algorithm
Wegweisende Quelle	Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Aliasnamen	REINFORCE, actor-critic, policy optimization, politika gradyanı	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Verwandt	4	3
Zusammenfassung	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.	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.