Analyzing Deep Reinforcement Learning for Robotics Control

Abstract

This research analyzes the application of Deep Reinforcement Learning (DRL) for robotics control, focusing on its potential to enhance the autonomy and efficiency of robotic systems. DRL, a powerful machine learning technique combining reinforcement learning with deep neural networks, allows robots to learn optimal control policies through interaction with their environment. This study aims to evaluate the effectiveness of DRL in various robotic control tasks, such as manipulation, navigation, and task execution. The research methodology involves developing and testing DRL algorithms on simulated robotic environments, using widely recognized frameworks such as OpenAI Gym and RoboSumo. The robots are trained to perform tasks by receiving feedback from their actions, which reinforces learning based on rewards and penalties. Data analysis involves comparing the performance of DRL models with traditional control methods, evaluating metrics such as task completion time, energy efficiency, and adaptability to dynamic environments. Results show that DRL-based systems significantly outperform conventional methods in complex, high-dimensional tasks, though challenges such as computational cost, reward shaping, and sample inefficiency remain. The study concludes that DRL has the potential to revolutionize robotics control, although further refinement of algorithms and resources is necessary to ensure their practical deployment in real-world applications.

Keywords: Deep Reinforcement Learning, robotics control, autonomous systems, machine learning, reinforcement learning, task execution, algorithm performance, robotic manipulation.