In this work, we present and study a training set-up that achieves fast
policy generation for real-world robotic tasks by using massive parallelism on
a single workstation GPU. We analyze and discuss the impact of different
training algorithm components in the massively parallel regime on the final
policy performance and training times. In addition, we present a novel
game-inspired curriculum that is well suited for training with thousands of
simulated robots in parallel. We evaluate the approach by training the
quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach
allows training policies for flat terrain in under four minutes, and in twenty
minutes for uneven terrain. This represents a speedup of multiple orders of
magnitude compared to previous work. Finally, we transfer the policies to the
real robot to validate the approach. We open-source our training code to help
accelerate further research in the field of learned legged locomotion.

本文介绍并研究了一种在单个工作站 GPU 上使用大量并行性实现快速生成真实世界机器人任务策略的训练设置，其中包括不同训练算法组件在大规模并行模式下对最终策略性能和训练时间的影响分析和讨论，同时还介绍了一种新颖的游戏启发课程，适用于数千个模拟机器人的并行训练，并通过将策略转换到真实机器人以验证方法的有效性。