Article
REINFORCEMENT LEARNING FOR AUTONOMOUS SYSTEM
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 trial-and-error exploration. This research presents a Reinforcement Learning-based Autonomous System implemented using a custom GridWorld environment integrated with the Deep Q-Network (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 decision-making. 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 real-world autonomous navigation scenarios. The DQN algorithm combines deep neural networks with Q-learning to estimate optimal action values and improve learning efficiency within high-dimensional 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 real-time 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 decision-making while reducing dependence on manually programmed rules. Furthermore, the framework establishes a strong foundation for future developments involving multi-agent reinforcement learning, explainable artificial intelligence, autonomous robotics, intelligent transportation systems, and real-world adaptive control applications.
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