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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Deep reinforcement learning is one kind of machine learning algorithms which uses the maximum cumulative reward to learn the optimal strategy. The difficulty is how to ensure the fast convergence of the model and generate a large number of sample data to promote the model optimization. Using the deep reinforcement learning framework of the AlphaZero algorithm, the deployment problem of wireless nodes in wireless ad hoc networks is equivalent to the game of Go. A deployment model of mobile nodes in wireless ad hoc networks based on the AlphaZero algorithm is designed. Because the application scenario of wireless ad hoc network does not have the characteristics of chessboard symmetry and invariability, it cannot expand the data sample set by rotating and changing the chessboard orientation. The strategy of dynamic updating learning rate and the method of selecting the latest model to generate sample data are used to solve the problem of fast model convergence.

Research on the Difficulty of Mobile Node Deployment’s Self-Play in Wireless Ad Hoc Networks Based on Deep Reinforcement Learning