השוואת שיטות
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| למידת חיזוק× | רשת נוירונים רקורנטית× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 1950s–1998 | 1986–1990 |
| הוגה השיטה≠ | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) | Rumelhart, D. E.; Elman, J. L. |
| סוג≠ | Sequential decision-making framework | Sequential neural network |
| מקור מכונן≠ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| כינויים | RL, reward-based learning, trial-and-error learning, policy optimization | RNN, Elman network, Jordan network, simple recurrent network |
| קשורות≠ | 2 | 3 |
| תקציר≠ | Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback. | 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. |
| ScholarGateמערך נתונים ↗ |
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