Automatic design of robot swarms that perform composite missions: an approach based on inverse reinforcement learning

We investigate the automatic design of robot swarms that perform composite missions—that is, missions specified as the composition of consecutive sub-missions. Automatic design through performance optimization has become a viable and appealing approach to designing robot swarms. First, a user defines a mission by specifying a performance measure: a function indicating to what extent the swarm has attained its goal. An optimization process then generates suitable control software for the robots by maximizing the performance measure. The definition of a performance measure is a challenging task that requires expert input, which hinders the automatic nature of the approach. Recently, inverse reinforcement learning was introduced to minimize the need for human intervention in the automatic design of robot swarms. However, this method was only applied to single-objective missions. In this paper, we extend the method to address composite missions, by formulating and solving the design problem as a multi-objective optimization problem. We conduct simulations with a swarm of twenty e-puck robots that perform twelve composite missions. We compare the performance of the swarm when the robots operate with control software produced manually or using inverse reinforcement learning.