论文标题
通过加强和应用SIM到现实来竞争现实世界，学习踢足球
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
论文作者
论文摘要
这项工作介绍了强化学习（RL）的应用，以完全控制IEEE非常小的足球足球（VSSS），这是拉丁美洲机器人竞赛（LARC）的传统联盟。在VSSS联赛中，两个小型机器人组成的两支球队相互对抗。我们提出了一个模拟环境，可以训练连续或离散的控制策略，以及一种模拟环境，一种模拟环境，一种允许使用所获得的策略控制机器人在现实世界中的机器人的方法。结果表明，学识渊博的政策表现出广泛的行为曲目，这些行为难以手工指定。这种称为VSSS-RL的方法能够在1-VS-1比赛中击败球队的前锋在2018年LARC中排名第三。
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in the Latin American Robotics Competition (LARC). In the VSSS league, two teams of three small robots play against each other. We propose a simulated environment in which continuous or discrete control policies can be trained, and a Sim-to-Real method to allow using the obtained policies to control a robot in the real world. The results show that the learned policies display a broad repertoire of behaviors that are difficult to specify by hand. This approach, called VSSS-RL, was able to beat the human-designed policy for the striker of the team ranked 3rd place in the 2018 LARC, in 1-vs-1 matches.
