Reinforcement Learning Applications in Autonomous Decision Making Systems

Mrs.G.Bruhaspathi¹, Thadisina Swetha²
¹Assistant Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College,
Hyderabad, India bruhaspathigudi@gmail.com
²Student, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India
swethareddythadisina2@gmail.com

.ABSTRACT:Reinforcement Learning has become a key technique for enabling autonomous decision-making systems to operate intelligently in complex and uncertain environments. Unlike traditional programming approaches, RL allows agents to learn optimal behaviours through continuous interaction with their surroundings by maximizing cumulative rewards. This makes it highly suitable for applications such as robotics, autonomous vehicles, industrial automation, healthcare decision support, and smart city systems, where real-time and adaptive decision-making is essential. In RL, an agent observes the state of the environment, takes actions, and receives feedback in the form of rewards or penalties, which guide future decisions. Advanced methods such as Q-learning, Policy Gradient methods, and Deep Reinforcement Learning (DRL) combine neural networks with RL to handle high-dimensional data and dynamic environments. These approaches enable systems to improve performance over time, adapt to changing conditions, and make efficient decisions without explicit human intervention.Despite its advantages, RL faces several challenges, including high computational cost, slow convergence, safety concerns, and the need for large amounts of training data. Recent research focuses on improving sample efficiency, ensuring safe exploration, and integrating RL with other techniques like supervised and unsupervised learning. Overall, the application of reinforcement learning in autonomous decision-making systems offers significant potential to enhance intelligence, adaptability, and automation across a wide range of real-world domains.Keywords: Reinforcement learning, Autonomous Systems, Decision Making, Deep Reinforcement Learning, Artificial Intelligence, Optimization, Adaptive Systems, Robotics, Smart Systems.