Task Partitioning and Scheduling Based on Stochastic Policy Gradient in Mobile Crowdsensing

Abstract

Deep reinforcement learning (DRL) has become prevalent for decision-making task assignments in mobile crowdsensing (MCS). However, when facing sensing scenarios with varying numbers of workers or task attributes, existing DRL-based task assignment schemes fail to generate matching policies continuously and are susceptible to environmental fluctuations. To overcome these issues, a twin-delayed deep stochastic policy gradient (TDDS) approach is presented for balanced and low-latency MCS task decomposition and parallel subtask allocation. A masked attention mechanism is incorporated into the policy network to enable TDDS to adapt to task-attribute and subtask variations. To enhance environmental adaptability, an off-policy DRL algorithm incorporating experience replay is developed to eliminate sample correlation during training. Gumbel-Softmax sampling is integrated into the twin-delayed deep deterministic policy gradient (TD3) to support discrete action space decisions and a customized reward strategy to reduce task completion delay and balance workloads. Extensive simulation results confirm that the proposed scheme outperforms mainstream DRL baselines in terms of environmental adaptability, task completion delay, and workload balancing.