Comparer des méthodes

	Q-Learning	Méthodes de gradient de politique
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992	1992
Auteur d'origine	Christopher Watkins & Peter Dayan	Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)
Type	Model-free reinforcement-learning control algorithm	Policy-based reinforcement learning
Source fondatrice	Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.	Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.
Alias	Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme	REINFORCE, actor-critic, policy optimization, politika gradyanı
Apparentées	3	4
Résumé	Q-learning, introduced by Christopher Watkins and Peter Dayan in 1992, is a model-free reinforcement-learning algorithm that learns the value of taking each action in each state — the Q-function — purely from experience, without a model of the environment. It is off-policy: it learns the optimal action-values while following an exploratory behaviour policy, and under standard conditions it provably converges to the optimal policy.	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.