Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Programação Dinâmica× | Aprendizado por Reforço Profundo× | |
|---|---|---|
| Área≠ | Otimização | Aprendizado profundo |
| Família≠ | Process / pipeline | Machine learning |
| Ano de origem≠ | 1957 | 2015 |
| Autor original≠ | Richard Bellman | Mnih, V. et al. (DQN) |
| Tipo≠ | Exact combinatorial optimization via recursive decomposition | Sequential decision-making (agent–environment interaction) |
| Fonte seminal≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6 | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ |
| Outros nomes≠ | DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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