Reinforcement Learning in Autonomous Systems: A Review of Algorithms and Applications

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm for enabling autonomous systems to learn and adapt to complex, dynamic environments. By leveraging the principles of trial and error, RL allows agents to optimize their behavior through interactions with their surroundings, receiving feedback in the form of rewards or penalties. This article provides a comprehensive review of RL algorithms and their applications in autonomous systems, including robotics, self-driving cars, and unmanned aerial vehicles (UAVs). These systems require the ability to make real-time decisions in unpredictable environments, making RL an ideal approach due to its adaptability and learning capabilities. The article begins by discussing the fundamental principles of RL, including key concepts such as Markov Decision Processes (MDPs), value functions, and policy optimization. It then explores state-of-the-art RL algorithms, such as Q-learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods, highlighting their strengths and limitations. For instance, while DQN has shown remarkable success in high-dimensional environments, it can struggle with continuous action spaces, which are better addressed by algorithms like PPO. To provide a structured overview, the article includes two tables summarizing key RL algorithms and their applications across various domains, offering readers a clear comparison of their features and use cases. Despite its potential, RL faces several challenges, including sample inefficiency, scalability, and safety concerns in real-world applications. The article concludes by discussing future research directions, such as improving sample efficiency through meta-learning, enhancing safety via robust RL techniques, and integrating RL with other machine learning paradigms like supervised and unsupervised learning. By addressing these challenges, RL can further advance the development of intelligent, autonomous systems capable of operating in increasingly complex and dynamic environments.