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Recent advances in deep Reinforcement Learning (RL) have createdunprecedented opportunities for intelligent automation, where a machine canautonomously learn an optimal policy for performing a given task. However,current deep RL algorithms predominantly specialize in a narrow range of tasks,are sample inefficient, and lack sufficient stability, which in turn hindertheir industrial adoption. This article tackles this limitation by developingand testing a Hyper-Actor Soft Actor-Critic (HASAC) RL framework based on thenotions of task modularization and transfer learning. The goal of the proposedHASAC is to enhance the adaptability of an agent to new tasks by transferringthe learned policies of former tasks to the new task via a "hyper-actor". TheHASAC framework is tested on a new virtual robotic manipulation benchmark,Meta-World. Numerical experiments show superior performance by HASAC overstate-of-the-art deep RL algorithms in terms of reward value, success rate, andtask completion time.

Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer