Salīdzināt metodes

	Q-apmācība	Dziļā pastiprinātā mācīšanās
Nozare	Mašīnmācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1992	2015
Autors	Christopher Watkins & Peter Dayan	Mnih, V. et al. (DQN)
Tips	Model-free reinforcement-learning control algorithm	Sequential decision-making (agent–environment interaction)
Pirmavots	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.
Citi nosaukumi	Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme	Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL
Saistītās	3	4
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.	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.