Robotic Control Mechanism Based on Deep Reinforcement Learning

Abstract

Deep reinforcement learning is a type of machine learning that enables an agent to learn from its interactions with the environment in order to maximize a reward signal. However, current research leaks of the combination with robotic control and the accuracy of robotic controlling illustrates low by utilizing the traditional detecting algorithms. In this research, the deep reinforcement learning algorithm is applied to control the characteristics of a robotic system, allowing it to adapt and improve its performance over time. The results of the research demonstrate that the deep reinforcement learning-based control mechanism is able to effectively navigate and manipulate objects in the robotic environment, achieving a high level of control and precision. Overall, this work shows the potential of deep reinforcement learning for creating advanced robotic control systems that can learn and adapt to changing conditions.