Application Requirements of Deep Reinforcement Learning Algorithms in Industrial Robots

Application Requirements of Deep Reinforcement Learning Algorithms in Industrial Robots

With the rapid development of intelligent manufacturing, the application demands for industrial robots in this field are growing increasingly, particularly in complex and dynamic manufacturing tasks where traditional control methods can no longer meet the requirements for high efficiency and precision. Deep reinforcement learning (DRL) algorithms, an advanced approach combining deep learning and reinforcement learning, provide a novel solution for the operational control of industrial robots. DRL algorithms enable interaction with the environment, continuously learning and optimizing control strategies to achieve high-precision control of industrial robot operations. They are particularly suitable for handling high-dimensional, continuous action spaces and complex state spaces, addressing the various uncertainties and complexities faced by industrial robots in practical operations.

This paper introduces the actor-critic algorithm model from deep reinforcement learning. By leveraging this model, intelligent operational control strategies are developed for industrial robots. The actor network is employed to generate action policies, while the critic network evaluates action values. Combined with a reward function, the network parameters are continuously optimized, enabling industrial robots to autonomously and efficiently complete tasks in complex manufacturing environments. This approach meets the demands of intelligent manufacturing for high efficiency, flexibility, and precision in industrial robot operations.