An enhanced Active Reinforcement Learning for Autonomous Robotics in Industrial automation

Abstract

An enhanced active reinforcement learning technique has been proposed to enable autonomous robots to operate and execute tasks in industrial automation. This approach combine hierarchical reinforcement learning and Bayesian optimization, to acquire knowledge from complex real-world environments and acquire optimal policies which can enable autonomous robots to perform collaborative tasks efficiently. The main advantage of this enhanced active reinforcement learning approach is the capability of the autonomous robot to autonomously adapt its movements and decision-making strategies when new tasks are required. It allows for the robot to explore its environment and learn how to complete tasks optimally while reducing the burden of manual intervention. Moreover, the proposed approach can generalize its knowledge to establish rewarding collaborative behaviors between robots and humans, thus allowing for collaborative human-robot interactions. This will be beneficial in performing industrial automation with robot cooperative tasks and optimize the efficiency of the industrial automation system..