Deep Reinforcement Learning (DRL) integrates the power of neural networks with the adaptability of trial-and-error learning, offering a transformative approach to teaching robots complex, real-world tasks. This paper investigates the potential of DRL to enhance robotic capabilities, focusing on its strengths in handling intricate decision-making, processing high-dimensional inputs, and adapting to changing environments. We review foundational concepts and prior studies, propose a detailed methodology for training a simulated robotic arm to perform a pick-and-place task, and present an in-depth analysis of the outcomes. Our results reveal significant improvements in task performance, yet they also expose persistent challenges such as lengthy training times and difficulties in real-world application. This work underscores DRL’s promise for robotics while identifying key areas for future improvement.
Volume 17 | Issue 1
Pages: 63-66