论文标题
在机器人足球中研究强化学习和模拟现实的框架
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
论文作者
论文摘要
本文介绍了一个名为VSSS-RL的开放式框架，用于研究强化学习（RL）和机器人足球中的SIM卡，重点是IEEE非常小的足球（VSSS）联赛。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，以控制足球代理的完整行为和基于域适应的SIM到现实方法，以使获得的策略适应真实的机器人。我们的结果表明，训练有素的政策学到了广泛的行为曲目，这些行为难以通过手工制作的控制政策实施。借助VSSS-RL，我们能够在2019年拉丁美洲机器人竞赛（LARC）中击败人类设计的政策，在21支球队中获得第四名，这是第一个在这项比赛中成功应用强化学习（RL）的人。环境和硬件规格都是开源的，可以允许我们的结果和进一步的研究可重复可重复。
This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VSSS) league. We propose a simulated environment in which continuous or discrete control policies can be trained to control the complete behavior of soccer agents and a sim-to-real method based on domain adaptation to adapt the obtained policies to real robots. Our results show that the trained policies learned a broad repertoire of behaviors that are difficult to implement with handcrafted control policies. With VSSS-RL, we were able to beat human-designed policies in the 2019 Latin American Robotics Competition (LARC), achieving 4th place out of 21 teams, being the first to apply Reinforcement Learning (RL) successfully in this competition. Both environment and hardware specifications are available open-source to allow reproducibility of our results and further studies.
