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空间索引动态规划：学习跟踪轨迹Space-indexed Dynamic Programming: Learning to Follow Trajectories |
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| 课程网址: | http://videolectures.net/icml08_kotler_sid/ |
| 主讲教师: | J. Zico Kolter |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the task of learning to accurately follow a trajectory in a vehicle such as a car or helicopter. A number of dynamic programming algorithms such as Differential Dynamic Programming (DDP) and Policy Search by Dynamic Programming (PSDP), can efficiently compute non-stationary policies for these tasks --- such policies in general are well-suited to trajectory following since they can easily generate different control actions at different times in order to follow the trajectory. However, a weakness of these algorithms is that their policies are time-indexed, in that they apply different policies depending on the current time. This is problematic since 1) the current time may not correspond well to where we are along the trajectory and 2) the uncertainty over future states can prevent these algorithms from finding any good policies at all. In this paper we propose a method for space-indexed dynamic programming that overcomes both these difficulties. We begin by showing how a dynamical system can be rewritten in terms of a spatial index variable (i.e., how far along the trajectory we are) rather than as a function of time. We then use these space-indexed dynamical systems to derive space-indexed version of the DDP and PSDP algorithms. Finally, we show that these algorithms perform well on a variety of control tasks, both in simulation and on real systems. |
| 关 键 词: | 动态编程算法; 非静态策略; 空间索引变量 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 62 |