Reinforcement Learning for Autonomous Systems: Studying reinforcement learning algorithms for training autonomous systems to make decisions in dynamic environments

Abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for training autonomous systems to make decisions in dynamic and uncertain environments. This paper provides a comprehensive overview of RL algorithms and their applications in autonomous systems. We discuss key concepts in RL, such as exploration-exploitation trade-offs, reward shaping, and policy optimization. We also review state-of-the-art RL algorithms, including deep Q-networks (DQN), policy gradient methods, and actor-critic models.