A Novel Blockchain-Reinforcement Learning Framework for Securing Wireless Sensor Networks with Energy Efficiency

Abstract

This paper introduces a novel approach to Reinforcement Learning (RL), focusing on the development and implementation of a Q-learning based algorithm. Reinforcement Learning, a critical branch of machine learning, enables agents to make decisions by interacting with their environment and learning from the consequences of their actions. Our study emphasizes the Q-learning model, a popular, model-free, off-policy algorithm that offers a robust framework for agents to learn optimal strategies in diverse settings. By iteratively updating the action-value function (Q-function) based on observed rewards and future reward estimations, our algorithm aims to achieve efficient learning and decision-making. This work contributes to the field by providing a detailed algorithmic structure, complete with mathematical formulations, that facilitates a deeper understanding of the Q-learning process and its practical applications in various domains.