Reinforcement Learning for Autonomous Systems
DOI:
https://doi.org/10.21590/Keywords:
Reinforcement Learning, Autonomous Systems, Deep Reinforcement Learning, Policy Optimization, Value-Based Learning, Safe RL, Robotics, Autonomous Vehicles, Simulation, Adaptive Control, High-Stakes Decision Making.Abstract
Reinforcement Learning (RL) is a machine learning paradigm in which agents learn optimal behaviors through interactions with their environment by maximizing cumulative rewards. In the context of autonomous systems, RL provides a powerful framework for decision-making, control, and adaptation in dynamic, uncertain, and complex environments. Applications span autonomous vehicles, drones, robotics, smart traffic management, and industrial automation.
This paper explores the principles of RL, including value-based, policy-based, and model-based approaches, and examines their integration into autonomous systems for real-time control and planning. Key challenges such as safety, sample efficiency, reward design, and generalization are discussed. Moreover, the paper highlights methods for combining RL with simulation, imitation learning, and safe exploration strategies to ensure reliability in high-stakes environments. Reinforcement Learning thus emerges as a critical enabler for autonomous systems capable of learning, adapting, and operating efficiently while minimizing human intervention.
