Salīdzināt metodes

	Q-apmācība	Dinamiskā programmēšana
Nozare	Mašīnmācīšanās	Optimizācija
Saime	Machine learning	Process / pipeline
Izcelsmes gads	1992	1957
Autors	Christopher Watkins & Peter Dayan	Richard Bellman
Tips	Model-free reinforcement-learning control algorithm	Exact combinatorial optimization via recursive decomposition
Pirmavots	Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6
Citi nosaukumi	Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama
Saistītās	3	3
Kopsavilkums	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.	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.