基于强化学习的停放车辆子任务卸载决策方法
By using reinforcement learning algorithms to construct a candidate set of vehicles in a parking lot and making task unloading decisions, the impact of the dynamics of parked vehicles on task unloading is resolved, thereby optimizing task completion latency and improving success rate.
CN116362500BActive Publication Date: 2026-07-17UNIV OF ELECTRONICS SCI & TECH OF CHINA
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNIV OF ELECTRONICS SCI & TECH OF CHINA
- Filing Date
- 2023-03-29
- Publication Date
- 2026-07-17
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Figure CN116362500B_ABST
Abstract
本发明公开了一种基于强化学习的停放车辆子任务卸载决策方法，通过无线接入点AP接收任务卸载请求，观测停车场内停放车辆，考虑停放车辆的停留稳定性，构建车辆候选集，AP将当前时刻的候选集车辆观测状态和任务属性输入到强化学习模型中进行训练，进行子任务的划分和卸载决策，再通过判断停放车辆的移动性对任务卸载的影响，如果因为车辆驶离导致任务无法及时完成，进行任务迁移保证任务及时完成。本发明的方法考虑车辆的停留稳定性，采用强化学习算法构建候选集对停放车辆进行筛选，降低车辆动态性对已有卸载决策的影响，使多个车辆能够协同完成子任务，优化任务卸载时延，同时采用任务迁移及时处理无法完成的子任务，降低任务失败率。
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