1KURSAM SNEHA, 2V. VANI 03 07 Autonomous systems have become a fundamental component of modern intelligent technologies owing to their capability to perform complex tasks with minimal or no human intervention These systems are extensively employed in robotics autonomous vehicles unmanned aerial vehicles industrial automation healthcare intelligent transportation and smart manufacturing However conventional autonomous systems primarily depend on predefined rules and static control strategies making them less effective in dynamic and uncertain environments Reinforcement Learning RL has emerged as a promising machine learning paradigm that enables autonomous agents to learn optimal behaviours through continuous interaction with their surroundings Unlike supervised learning RL does not require labelled datasets instead it learns by maximising cumulative rewards obtained through trialanderror exploration This research presents a Reinforcement Learningbased Autonomous System implemented using a custom GridWorld environment integrated with the Deep QNetwork DQN algorithm The proposed framework enables an autonomous agent to observe environmental states execute actions receive reward feedback and continuously refine its policy to achieve optimal decisionmaking A Flaskbased web application is developed to provide an interactive interface for user authentication training management model evaluation dashboard analytics and reinforcement learning model storage The environment incorporates obstacles target locations and navigation paths that simulate realworld autonomous navigation scenarios The DQN algorithm combines deep neural networks with Qlearning to estimate optimal action values and improve learning efficiency within highdimensional state spaces Experimental evaluation demonstrates that the autonomous agent gradually improves navigation efficiency obstacle avoidance capability cumulative reward and policy convergence through repeated training episodes The framework provides realtime monitoring of training performance reward progression and model evaluation metrics thereby enhancing transparency and usability The proposed system offers a scalable adaptive and intelligent platform that significantly improves autonomous decisionmaking while reducing dependence on manually programmed rules Furthermore the framework establishes a strong foundation for future developments involving multiagent reinforcement learning explainable artificial intelligence autonomous robotics intelligent transportation systems and realworld adaptive control applications Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026