| 基于Q学习的任务调度问题的改进研究 |
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| 引用本文： | 刘晓平,杜琳,石慧. 基于Q学习的任务调度问题的改进研究[J]. 工程图学学报, 2012, 33(3): 11-16 |
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| 作者姓名： | 刘晓平 杜琳 石慧 |
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| 作者单位： | 合肥工业大学计算机与信息学院,安徽合肥,230009 |
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| 基金项目： | 国家自然科学基金资助项目，合肥工业大学自主创新资助项目 |
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| 摘 要： | 论文针对协同工作中的任务调度问题,建立了相应的马尔可夫决策过程模型,在此基础上提出了一种改进的基于模拟退火的Q学习算法。该算法通过引入模拟退火,并结合贪婪策略,以及在状态空间上的筛选判断,显著地提高了收敛速度,缩短了执行时间。最后与其它文献中相关算法的对比分析,验证了本改进算法的高效性。
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| 关 键 词： | 任务调度 Q学习 强化学习 模拟退火 |
Improvement of task scheduling based on Q-learning |
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| Liu Xiaoping , Du Lin , Shi Hui. Improvement of task scheduling based on Q-learning[J]. Journal of Engineering Graphics, 2012, 33(3): 11-16 |
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| Authors: | Liu Xiaoping Du Lin Shi Hui |
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| Affiliation: | (School of Computer and Information,Hefei University of Technology,Heifei Anhui 230009,China) |
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| Abstract: | In this paper,a Markov Decision Process model is built to describe the problem of task scheduling in cooperative work,and a improved Q-learning algorithm based on Metropolis rule is present to solve the problem.In the algorithm,Metropolis rule combined with Greedy Strategy is introduced and a selection in state space is adopted,which accelerate the convergence,and shorten the running time.Finally,the algorithm is compared to some related algorithms of other papers,and the algorithm performance is analyzed as well,which indicates the efficiency of the improved Q-learning algorithm. |
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| Keywords: | task scheduling Q-learning reinforcement learning simulated annealing |
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