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马尔可夫决策过程的近最佳Chernoff界Near Optimal Chernoff Bounds for Markov Decision Processes |
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| 课程网址: | http://videolectures.net/machine_mihai_moldovan_processes/ |
| 主讲教师: | Teodor Mihai Moldovan |
| 开课单位: | 加州大学伯克利分校 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The expected return is a widely used objective in decision making under uncertainty. Many algorithms, such as value iteration, have been proposed to optimize it. In risk-aware settings, however, the expected return is often not an appropriate objective to optimize. We propose a new optimization objective for risk-aware planning and show that it has desirable theoretical properties. We also draw connections to previously proposed objectives for risk-aware planing: minmax, exponential utility, percentile and mean minus variance. Our method applies to an extended class of Markov decision processes: we allow costs to be stochastic as long as they are bounded. Additionally, we present an efficient algorithm for optimizing the proposed objective. Synthetic and real-world experiments illustrate the effectiveness of our method, at scale. |
| 关 键 词: | 预期回报; 风险意识规划; 马尔可夫决策 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：lxf |
| 阅读次数: | 57 |