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 to provide the service based on LFRL: www.shared-robotics.com. and Technology. eelium@ust.hk 3 Boyi liu is also with the University of Chinese Academy of Sciences. Actor1: The next step may have little effect. Actor3:For the next step, there are two better choices. Actor2: For the next step, there is a very certain decision. ¼¼ The man needs to make decisions based on the rules of the chess, his chess experience and the chess experience of others he has seen.
REF studied the FRL settings in the autonomous navigation where the main task is to make the robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments.
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
IEEE Robotics and Automation Letters, Volume: 4, Issue:4, On Page(s): 4555-4562, 2019
Engineering Computer Science