方法对比
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| 贝叶斯动态规划× | 强化学习× | |
|---|---|---|
| 领域≠ | 仿真 | 深度学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1957 (Bellman DP); Bayesian extensions 1990s–2000s | 1950s–1998 |
| 提出者≠ | Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas) | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| 类型≠ | Sequential optimization with Bayesian belief updating | Sequential decision-making framework |
| 开创性文献≠ | Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267 | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| 别名 | BDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control | RL, reward-based learning, trial-and-error learning, policy optimization |
| 相关≠ | 4 | 2 |
| 摘要≠ | Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration. | 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. |
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