分布式Q学习多目标函数优化策略

将分布式Q学习算法与Pareto排序法相结合，提出了一种利用强化学习算法解决多目标优化问题的策略。该策略充分利用Q学习语句式的奖赏机制来描述问题的多重目标函数，并结合一般的Pareto排序法，在有限的迭代过程后输出可以充分接近于Pareto前沿的非支配解集。与其他智能搜索算法相比，该策略具有结构简单、无需先验知识、参数设置少的特点。测试函数优化问题验证了算法的有效性，为智能算法解决多目标优化问题提供了一种新思路。

A Q-learning based multi-objective function optimization strategy

In this paper,a multi-optimization strategy is proposed based on combining the Q-learning algorithm and Pareto sorting.Multiple objective functions of the problem are described with the help of a Q-learning rewards strategy.Combined with Pareto sorting,the proposed strategy generates a non-dominated solution set close enough to a real Pareto front after limited iterations.Compared with other intelligent algorithms,it offers the advantages of a simpler structure,learning without prior knowledge,and fewer parameters.The results with test functions prove the validity of the proposed strategy.This method therefore provides an alternative means of intelligent optimization in this area.