Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This addresses the challenge of efficient knowledge sharing and adaptation in cloud robotic systems, representing an incremental advancement in federated reinforcement learning for robotics.
The paper tackles the problem of enabling robots to fuse and transfer experience for faster adaptation to new environments by proposing Lifelong Federated Reinforcement Learning (LFRL), an architecture for navigation in cloud robotic systems. Experiments show LFRL greatly improves reinforcement learning efficiency for robot navigation and demonstrates capability in fusing prior knowledge.
This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.