Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Aprendizaje por Refuerzo Profundo× | Aprendizaje por transferencia× | |
|---|---|---|
| Campo≠ | Aprendizaje profundo | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2015 | 2010 (formalized); 1990s (early roots) |
| Autor original≠ | Mnih, V. et al. (DQN) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Tipo≠ | Sequential decision-making (agent–environment interaction) | Learning paradigm |
| Fuente seminal≠ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Alias≠ | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateConjunto de datos ↗ |
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