Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots

Abstract

A mobile sensing robot team (MSRT) is a typical application of multi-agent systems. This paper investigates multiagent reinforcement learning in the MSRT problem. A naive coordinated learning approach is first proposed that uses a coordination graph to model interaction relationships among robots. To further reduce the computation complexity in the context of continuously changing topology caused by robots' movement, we then propose an on-line transfer learning method that is capable of transferring the past interaction experience and learned knowledge to a new context in a dynamic environment. Simulations verify that the method can achieve reasonable team performance by properly balancing robots' local selfish interests and global team performance.