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This paper was motivated by the problem of how to make robots fuse andtransfer their experience so that they can effectively use prior knowledge andquickly adapt to new environments. To address the problem, we present alearning architecture for navigation in cloud robotic systems: LifelongFederated Reinforcement Learning (LFRL). In the work, We propose a knowledgefusion algorithm for upgrading a shared model deployed on the cloud. Then,effective transfer learning methods in LFRL are introduced. LFRL is consistentwith human cognitive science and fits well in cloud robotic systems.Experiments show that LFRL greatly improves the efficiency of reinforcementlearning for robot navigation. The cloud robotic system deployment also showsthat LFRL is capable of fusing prior knowledge. In addition, we release a cloudrobotic navigation-learning website based on LFRL.

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems