Swarm Reinforcement Learning Method for a Multi-robot Formation Problem

Abstract

In this paper, we treat a multi-robot formation problem in which each of multiple robots selects one of goal positions adequately and finds the optimal route to the goal position, and we propose a swarm reinforcement learning method for acquiring the optimal policy in the problem. In the proposed method, multiple sets of the robots and an environment, which are called learning worlds, are prepared and the robots in each learning world learn not only by performing a usual reinforcement learning method but also by exchanging information among learning worlds. The performance of the proposed method is evaluated through numerical experiments.