Learning queuing strategies in human-multi-robot interaction

We consider multi-robot applications, where a team of robots can ask for the intervention of a human operator to handle difficult situations. As the number of requests grows, team members will have to wait for the operator attention, hence the operator becomes a bottleneck for the system. In contrast to previous work we consider a balking queue model where robots can decide either to join the queue or balk (leave the queue). Our aim is to devise an approach that allows the robots to learn cooperative balking strategies to decrease the time spent waiting for the operator. In more detail, we formalize the problem as Decentralized Markov Decision Process (Dec-MDP) and provide a scalable state representation by adding the state of the queue as an extra feature to each robot’s local observation. We then apply multi-agent reinforcement learning to solve the model and evaluate aour approach on a simulated scenario.