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Complex operational scenarios increasingly demand that industrial robots sequentially resolve multiple interrelated problems to accomplish complex operational tasks, necessitating robots to have the capacity for not only learning through interaction with the environment but also for continual learning. Current deep reinforcement learning methods have demonstrated substantial prowess in enabling robots to learn individual simple operational skills. However, catastrophic forgetting regarding the continual learning of various distinct tasks under a unified control policy remains a challenge. The lengthy sequential decision-making trajectory in reinforcement learning scenarios results in a massive state-action search space for the agent. Moreover, low-value state-action samples exacerbate the difficulty of continuous learning in reinforcement learning problems. In this letter, we propose a Continual Reinforcement Learning (CRL) method that accommodates the incremental multiskill learning demands of robots. We transform the tightly coupled structure in Guided Policy Search (GPS) algorithms, which closely intertwine local and global policies, into a loosely coupled structure. This revised structure updates the global policy only after the local policy for a specific task has converged, enabling online learning. In incrementally learning new tasks, the global policy is updated using hard parameter sharing and Memory Aware Synapses (MAS), creating task-specific layers while penalizing significant parameter changes in shared layers linked to prior tasks. This method reduces overfitting and mitigates catastrophic forgetting in robotic CRL. We validate our method on PR2, UR5 and Sawyer robots in simulators as well as on a real UR5 robot.
Dong et al. (Tue,) studied this question.