Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическое программирование× | Методы градиента политики× | |
|---|---|---|
| Область≠ | Оптимизация | Машинное обучение |
| Семейство≠ | Process / pipeline | Machine learning |
| Год появления≠ | 1957 | 1992 |
| Автор метода≠ | Richard Bellman | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) |
| Тип≠ | Exact combinatorial optimization via recursive decomposition | Policy-based reinforcement learning |
| Основополагающий источник≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6 | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ |
| Другие названия | DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama | REINFORCE, actor-critic, policy optimization, politika gradyanı |
| Связанные≠ | 3 | 4 |
| Сводка≠ | Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure. | 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 ↗ |
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