Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network

TL;DR

This survey reviews multi-agent Q-Learning algorithms and game theory frameworks applied to resource management in wireless sensor networks, highlighting challenges and future research directions.

Contribution

It provides a comprehensive overview of multi-agent Q-Learning methods and game-theoretic frameworks specifically for wireless sensor network resource management.

Findings

Survey of various game-theoretic frameworks used

Identification of key challenges in multi-agent Q-Learning

Discussion of future research directions

Abstract

This report aims to survey multi-agent Q-Learning algorithms, analyze different game theory frameworks used, address each framework's applications, and report challenges and future directions. The target application for this study is resource management in the wireless sensor network. In the first section, the author provided an introduction regarding the applications of wireless sensor networks. After that, the author presented a summary of the Q-Learning algorithm, a well-known classic solution for model-free reinforcement learning problems. In the third section, the author extended the Q-Learning algorithm for multi-agent scenarios and discussed its challenges. In the fourth section, the author surveyed sets of game-theoretic frameworks that researchers used to address this problem for resource allocation and task scheduling in the wireless sensor networks. Lastly, the author mentioned some interesting open challenges in this domain.