Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Policy gradient-metoder× | Återkommande neuralt nätverk× | |
|---|---|---|
| Ämnesområde≠ | Maskininlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 1992 | 1986–1990 |
| Upphovsperson≠ | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) | Rumelhart, D. E.; Elman, J. L. |
| Typ≠ | Policy-based reinforcement learning | Sequential neural network |
| Ursprungskälla≠ | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Alias | REINFORCE, actor-critic, policy optimization, politika gradyanı | RNN, Elman network, Jordan network, simple recurrent network |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms. | 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. |
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