Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Djup förstärkningsinlärning× | Återkommande neuralt nätverk× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2015 | 1986–1990 |
| Upphovsperson≠ | Mnih, V. et al. (DQN) | Rumelhart, D. E.; Elman, J. L. |
| Typ≠ | Sequential decision-making (agent–environment interaction) | Sequential neural network |
| Ursprungskälla≠ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Alias≠ | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL | RNN, Elman network, Jordan network, simple recurrent network |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | 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. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGateDatamängd ↗ |
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