グラフ方策勾配による大規模分散協調型ラベルなし動作計画
Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
多数ロボットのラベルなし動作計画をマルチエージェント問題として定式化し、グラフニューラルネットで方策をパラメータ化することで、少人数で学習したフィルタを大規模系へゼロショット転移できる手法を提案した。
著者: Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
分類: cs.RO, cs.LG
原文アブストラクト
In this paper, we present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. To solve the problem of arbitrary dynamics and constraints we propose formulating the problem as a multi-agent problem. We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots. The GNN reduces the dimensionality of the problem by learning filters that aggregate information among robots locally, similar to how a convolutional neural network is able to learn local features in an image. Additionally, by employing a GNN we are also able to overcome the computational overhead of training policies for a large number of robots by first training graph filters for a small number of robots followed by zero-shot policy transfer to a larger number of robots. We demonstrate the effectiveness of our framework through various simulations.
