摘要
深度强化学习在于将深度学习的感知能力与强化学习的决策能力相结合,可以直接根据输入进行控制,是一种更接近人类思维方式的人工智能方法。旨在二者结合基础上,研究了一种基于深度强化学习的资源调度算法的设计框架。该框架首先利用从网络节点获取的大量先验数据,训练深度学习网络;然后利用强化学习来分配网络资源;接着通过大量的自我对弈,实现基于深度强化学习的价值网络学习。最后,设计实验方案对算法的性能进行了仿真和对比验证,以验证该算法的有效性。
Depth intensive study is a combination of deep learning perceived ability and enhanced learning decision-making ability which can be controlled by the input. Depth intensive study is an artificial intelligence method which is closer to human thinking. Based on the combination of the two methods, the paper studies a designed framework of resource scheduling algorithm based on depth intensive study. First, the framework utilizes a large number of priori data from the network nodes to train depth learning network. Then use the enhanced learning to allocate network resources, Next realize the value of network learning based on deep reinforcement learning through a lot of self-chess. Finally, the performance of the algorithm is simulated and compared, and the results confirm the effectiveness of the algorithm.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2017年第6期1047-1053,共7页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(U1609216)资助
关键词
深度学习
调度算法
蒙特卡洛模拟
强化学习
deep learning
scheduling algorithms
Monte Carlo simulation
reinforcement learning
