大規模ロボット制御のためのグラフ方策勾配法
Graph Policy Gradients for Large Scale Robot Control
多数の同種ロボットを制御するため、ロボット間のグラフ対称性を利用したグラフ畳み込みネットワークで方策をパラメータ化するGraph Policy Gradientsを提案し、3台で学習した方策を100台以上にゼロショット転移できることを示した。
著者: Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar
分類: cs.RO
原文アブストラクト
In this paper, we consider the problem of learning policies to control a large number of homogeneous robots. To this end, we propose a new algorithm we call Graph Policy Gradients (GPG) that exploits the underlying graph symmetry among the robots. The curse of dimensionality one encounters when working with a large number of robots is mitigated by employing a graph convolutional neural (GCN) network to parametrize policies for the robots. The GCN 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. Through experiments on formation flying, we show that our proposed method is able to scale better than existing reinforcement methods that employ fully connected networks. More importantly, we show that by using our locally learned filters we are able to zero-shot transfer policies trained on just three robots to over hundred robots.
