方法对比
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| 多目标马尔可夫模型× | 随机动态规划× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2006 | 1957 |
| 提出者≠ | Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| 类型≠ | Stochastic sequential decision model with multiple objectives | Sequential optimization under uncertainty |
| 开创性文献≠ | Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113. DOI ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| 别名 | MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model | SDP, Markov Decision Process, MDP, Stochastic DP |
| 相关≠ | 5 | 6 |
| 摘要≠ | A Multi-objective Markov Model (MOMDP) extends classical Markov Decision Processes to settings where an agent must optimize several reward signals simultaneously. Instead of a single optimal policy, the model produces a Pareto-optimal set of policies, enabling decision-makers to navigate trade-offs between competing goals such as cost, risk, and throughput over time. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
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