Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обучение с подкреплением× | Методы градиента политики× | |
|---|---|---|
| Область≠ | Глубокое обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1950s–1998 | 1992 |
| Автор метода≠ | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) |
| Тип≠ | Sequential decision-making framework | Policy-based reinforcement learning |
| Основополагающий источник≠ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ |
| Другие названия | RL, reward-based learning, trial-and-error learning, policy optimization | REINFORCE, actor-critic, policy optimization, politika gradyanı |
| Связанные≠ | 2 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
|
|