Reinforcement Learning-Based Control Strategies for Autonomous Robotic Systems

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.