Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Q-Learning× | Diepgaand bekrachtigingsleren× | |
|---|---|---|
| Vakgebied≠ | Machine learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1992 | 2015 |
| Grondlegger≠ | Christopher Watkins & Peter Dayan | Mnih, V. et al. (DQN) |
| Type≠ | Model-free reinforcement-learning control algorithm | Sequential decision-making (agent–environment interaction) |
| Oorspronkelijke bron≠ | Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292. DOI ↗ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ |
| Aliassen≠ | Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | 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. |
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