Reinforcement Learning-Based Control Strategies for Autonomous Robotic Systems

Authors

  • Amita Contractor Department of Computer Engineering, Parul Polytechnic Institute, Parul University, India
  • Vaibhavi Pandya Department of Computer Engineering, Parul Polytechnic Institute, Parul University, India
  • Kalpana Prajapati Department of Computer Engineering, Parul Polytechnic Institute, Parul University, India
  • Poonam Faldu Department of Computer Engineering, Parul Polytechnic Institute, Parul University, India

DOI:

https://doi.org/10.63278/1449

Keywords:

Reinforcement Learning (RL), Robotic Control, Deep Q-Networks (DQN), Policy Optimization, Autonomous Systems

Abstract

This research looks into the implementation of reinforcement learning (RL) principles into robotics with a particular interest in controlling autonomous systems. It examines various RL methods, such as Proximal Policy Optimization (PPO), and Deep Q-Networks (DQN), to improve robotic decision-making processes. Using information from Fiber Bragg Grating (FBG) sensors, we focus on real-time processing of sensor data and demonstrate greater levels of efficiency and flexibility within active settings. Quantitative data illustrates notable advancements in stability, accuracy, and spatial economic efficiency, which further the development of robotic autonomy.

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Published

2025-04-16

How to Cite

Amita Contractor, Vaibhavi Pandya, Kalpana Prajapati, and Poonam Faldu. 2025. “Reinforcement Learning-Based Control Strategies for Autonomous Robotic Systems ”. Metallurgical and Materials Engineering 31 (4):394-98. https://doi.org/10.63278/1449.

Issue

Vol. 31 No. 4 (2025)

Section

Research

License

Copyright (c) 2025 Amita Contractor, Vaibhavi Pandya, Kalpana Prajapati, Poonam Faldu

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