Porovnat metody

	Q-učení	Hluboké zpatňované učení
Obor	Strojové učení	Hluboké učení
Rodina	Machine learning	Machine learning
Rok vzniku	1992	2015
Tvůrce	Christopher Watkins & Peter Dayan	Mnih, V. et al. (DQN)
Typ	Model-free reinforcement-learning control algorithm	Sequential decision-making (agent–environment interaction)
Původní zdroj	Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.	Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.
Další názvy	Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL
Příbuzné	3	4
Shrnutí	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.	Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return.