Robotic Manipulators: A Control Architecture Approach Based on Deep Reinforcement Learning and Simulation Environments

With the rapid technological advancement and the increasing need for more intelligent robotic systems in various work areas, it is essential that such systems exhibit improved robustness and control characteristics. In this context, Deep Reinforcement Learning has become a key technique for developing more accurate and efficient control models in robotics. Deep Reinforcement Learning involves training models from a simulated environment that allow different scenarios to be explored without the limitations and constraints of the real world. This allows robotics engineers and developers to fine-tune and improve the control systems of robotic manipulators, making them more reliable and effective in their work. In this article, we present an overview of the main tools, algorithms and simulators used for end-to-end control of robotic manipulators. The use of simulators and Deep Reinforcement Learning techniques in robotic manipulator training allows developers to train robots safely and effectively, without having to deal with the costs and risks associated with training robots in real environments. In addition, concrete examples and applications are included that illustrate how these techniques can be applied to improve the performance and efficiency of robotic manipulator control.