Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network
This is an incremental survey paper for researchers in wireless sensor networks and multi-agent reinforcement learning.
This survey paper reviews multi-agent Q-Learning algorithms and game theory frameworks for resource management in wireless sensor networks, analyzing applications, challenges, and future directions without presenting new experimental results.
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.