競争を通じてロボットの潜在能力を引き出す
Stimulate the Potential of Robots via Competition
競争環境での他者との比較を学習の補助信号として活用し、ロボットが競争から知識を得て潜在能力を最大限に発揮する競争学習フレームワークを提案。マルチエージェント競走環境での実験で、単一ロボット環境の最先端手法より優れた性能を示した。
著者: Kangyao Huang, Di Guo, Xinyu Zhang, Xiangyang Ji, Huaping Liu
分類: cs.RO, cs.AI
原文アブストラクト
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents. Although we might get anxious under the pressure, it could also be a drive for us to stimulate our potentials to the best in order to keep up with others. Inspired by this, we propose a competitive learning framework which is able to help individual robot to acquire knowledge from the competition, fully stimulating its dynamics potential in the race. Specifically, the competition information among competitors is introduced as the additional auxiliary signal to learn advantaged actions. We further build a Multiagent-Race environment, and extensive experiments are conducted, demonstrating that robots trained in competitive environments outperform ones that are trained with SoTA algorithms in single robot environment.
