Adaptable automation with modular deep reinforcement learning and policy transfer

Abstract

Future industrial automation systems are anticipated to be shaped by intelligent technologies that allow for the adaptability of machines to the variations and uncertainties in processes and work environments. This paper is motivated by the need for devising new intelligent methods that enable efficient and scalable training of collaborative robots on a variety of tasks that foster their adaptability to new tasks and environments. Recent advances in deep Reinforcement Learning (RL) provide new possibilities to realize this vision. The state-of-the-art in deep RL offers proven algorithms that enable autonomous learning and mastery of a variety of robotic manipulation tasks with minimal human intervention. However, current deep RL algorithms predominantly specialize in a narrow range of tasks, are sample inefficient, and lack sufficient stability, which hinders their adoption in real-life, industrial settings. This paper develops and tests a Hyper-Actor Soft Actor–Critic (HASAC) deep RL framework based on the notions of task modularization and transfer learning to tackle this limitation. The goal of the proposed HASAC is to enhance an agent’s adaptability to new tasks by transferring the learned policies of former tasks to the new task through a ”hyper-actor”. The HASAC framework is tested on the virtual robotic manipulation benchmark, Meta-World. Numerical experiments indicate superior performance by HASAC over state-of-the-art deep RL algorithms in terms of reward value, success rate, and task completion time.