基于深度强化学习的办公流程任务分配优化

In the office platform, we often need to face a large number of parallel heterogeneous process tasks. This not only tests the ability of task executors but also puts forward requirements for the performance of the scheduling system. The multi-agent game model based on Markov game theory is proposed in this paper, which adopts the reinforcement learning (RL) approach along with quantitative analysis of the degree of cooperation and relaxation. This model realizes the optimal scheduling system with the overall process degree and maximum completion time as the optimization objectives and enhances the overall execution efficiency. Finally, to confirm the efficacy of this approach, the meta-heuristic algorithm based on ant colony and the reinforcement learning algorithm based on D3QN and deep reinforcement learning (DRL) are contrasted using the real business system process as the experimental data and the identical optimization targets.