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

## TL;DR

This paper introduces Lifelong Federated Reinforcement Learning (LFRL), a novel architecture enabling robots to share, fuse, and transfer knowledge efficiently for improved navigation in cloud robotic systems.

## Contribution

The paper proposes a new LFRL architecture with a knowledge fusion algorithm and transfer learning methods, enhancing robot navigation and knowledge sharing in cloud robotics.

## Key findings

- LFRL significantly improves reinforcement learning efficiency for robot navigation.
- LFRL effectively fuses prior knowledge in cloud robotic systems.
- Deployment demonstrates practical feasibility of LFRL in real-world scenarios.

## Abstract

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.