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.

翻译：在竞争环境中，我们常常感受到压力，这种压力源于与他人或对手比较时对成功的渴望。虽然压力可能引发焦虑，但也能激励我们激发最大潜力以追赶他人。受此启发，我们提出了一种竞争学习框架，该框架能够帮助单个机器人从竞争中获取知识，充分激发其动态潜力。具体而言，我们将竞争者之间的竞争信息作为额外的辅助信号引入，以学习更优势的动作。我们进一步构建了多智能体竞速环境（Multiagent-Race environment），并进行了大量实验，结果表明，在竞争环境中训练的机器人性能优于在单一机器人环境中使用当前最优（SoTA）算法训练的机器人。