Distributed neural network-based policy gradient reinforcement learning for multi-robot formations

Multi-robot learning is a challenging task not only because of large and continuous state/action spaces, but also uncertainty and partial observability during learning. This paper presents a distributed policy gradient reinforcement learning (PGRL) methodology of a multi-robot system using neural network as the function approximator. This distributed PGRL algorithm enables each robot to independently decide its policy, which is, however, affected by all the other robots. Neural network is used to generalize over continuous state space as well as discrete/continuous action spaces. A case study on leader-follower formation application is performed to demonstrate the effectiveness of the proposed learning method.