English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In queueing networks, it is well known that the throughput-optimalbackpressure routing algorithm results in poor delay performance for light andmoderate traffic loads. To improve delay performance, state-of-the-artbackpressure routing algorithm (called BPmin [1]) exploits queue lengthinformation to direct packets to less congested routes to their destinations.However, BPmin algorithm estimates route congestion based on unrealisticassumption that every node in the network knows real-time global queue lengthinformation of all other nodes. In this paper, we propose multi-agentQ-learning aided backpressure routing algorithm, where each node estimatesroute congestion using only local information of neighboring nodes. Ouralgorithm not only outperforms state-of-the-art BPmin algorithm in delayperformance but also retains the following appealing features: distributedimplementation, low computation complexity and throughput-optimality.Simulation results show our algorithm reduces average packet delay by 95% forlight traffic loads and by 41% for moderate traffic loads when compared tostate-of-the-art BPmin algorithm.

Multi-Agent Q-Learning Aided Backpressure Routing Algorithm for Delay Reduction