Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and
efficient decision-making, which is often achieved using heuristics-aided
methods such as genetic algorithms, auction-based methods, and bipartite graph
matching methods. These methods often assume a form that lends better
explainability compared to an end-to-end (learnt) neural network based policy
for MRTA. However, deriving suitable heuristics can be tedious, risky and in
some cases impractical if problems are too complex. This raises the question:
can these heuristics be learned? To this end, this paper particularly develops
a Graph Reinforcement Learning (GRL) framework to learn the heuristics or
incentives for a bipartite graph matching approach to MRTA. Specifically a
Capsule Attention policy model is used to learn how to weight task/robot
pairings (edges) in the bipartite graph that connects the set of tasks to the
set of robots. The original capsule attention network architecture is
fundamentally modified by adding encoding of robots' state graph, and two
Multihead Attention based decoders whose output are used to construct a
LogNormal distribution matrix from which positive bigraph weights can be drawn.
The performance of this new bigraph matching approach augmented with a
GRL-derived incentive is found to be at par with the original bigraph matching
approach that used expert-specified heuristics, with the former offering
notable robustness benefits. During training, the learned incentive policy is
found to get initially closer to the expert-specified incentive and then
slightly deviate from its trend.

该论文介绍了一种使用图强化学习 (GRL) 框架来学习多机器人任务分配 (MRTA) 的启发式或激励的方法，其中使用胶囊注意力策略模型来学习如何赋予任务 / 机器人配对 (边) 在将任务集与机器人集连接的二分图中的权重。该方法与使用专家指定启发式的原始二分图匹配方法性能相当，但具有显著的鲁棒性优势。在训练过程中，学到的激励策略与专家指定的激励策略初始接近，然后稍微偏离其趋势。