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首页> 外文期刊>International journal of business data communications and networking >Adaptive Routing for an Ad Hoc Network Based on Reinforcement Learning
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Adaptive Routing for an Ad Hoc Network Based on Reinforcement Learning

机译：基于强化学习的Ad Hoc网络自适应路由

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摘要

This paper describes and evaluates the performance of various reinforcement learning algorithms with shortest path algorithms that are widely used for routing packets throughout the network. Shortest path routing is simplest policy used for routing the packets along the path having minimum number of hops. In high traffic or high mobility conditions, the shortest path gets flooded with huge number of packets and congestions occurs, so such shortest path does not provide the shortest path and increases delay for reaching the packets to the destination. Reinforcement learning algorithms are adaptive algorithms where the path is selected based on the traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on a 6-by-6 irregular grid and sample ad hoc network shows that performance parameters used for judging the network such as packet delivery ratio and delay provide optimum results using reinforcement learning algorithms.
机译：本文使用最短路径算法描述和评估了各种强化学习算法的性能，这些算法被广泛用于在整个网络中路由数据包。最短路径路由是最简单的策略，用于沿着具有最小跳数的路径路由数据包。在高业务量或高移动性条件下，最短路径充斥着大量数据包，并发生拥塞，因此，这种最短路径无法提供最短路径，并增加了将数据包到达目的地的延迟。强化学习算法是自适应算法，其中，基于网络上实时存在的流量来选择路径。因此，它们保证了将数据包到达目的地的最短交付时间。在6×6不规则网格上进行的分析和示例性ad hoc网络显示，用于判断网络的性能参数（如数据包传输率和延迟）使用强化学习算法可提供最佳结果。

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