Advances in Reinforcement Learning for Autonomous Systems

Abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for training autonomous systems, enabling machines to learn optimal decision-making strategies through interaction with their environment. Recent advances in RL have significantly improved the capabilities of autonomous systems in complex and dynamic environments, including robotics, autonomous vehicles, and smart grids. This article explores the latest developments in reinforcement learning techniques, including deep reinforcement learning, multi-agent RL, and inverse reinforcement learning, and their applications in autonomous systems. The paper also discusses the challenges, opportunities, and future directions for research in this area.