  This paper presents a novel graph reinforcement learning (RL) architecture to
solve multi-robot task allocation (MRTA) problems that involve tasks with
deadlines and workload, and robot constraints such as work capacity. While
drawing motivation from recent graph learning methods that learn to solve
combinatorial optimization (CO) problems such as multi-Traveling Salesman and
Vehicle Routing Problems using RL, this paper seeks to provide better
performance (compared to non-learning methods) and important scalability
(compared to existing learning architectures) for the stated class of MRTA
problems. The proposed neural architecture, called Capsule Attention-based
Mechanism or CapAM acts as the policy network, and includes three main
components: 1) an encoder: a Capsule Network based node embedding model to
represent each task as a learnable feature vector; 2) a decoder: an
attention-based model to facilitate a sequential output; and 3) context: that
encodes the states of the mission and the robots. To train the CapAM model, the
policy-gradient method based on REINFORCE is used. When evaluated over unseen
scenarios, CapAM demonstrates better task completion performance and $>$10
times faster decision-making compared to standard non-learning based online
MRTA methods. CapAM's advantage in generalizability, and scalability to test
problems of size larger than those used in training, are also successfully
demonstrated in comparison to a popular approach for learning to solve CO
problems, namely the purely attention mechanism.
