Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Rudin, Nikita, Hoeller, David, Reist, Philipp, Hutter, Marco
Format: Article
Language:English
Subjects:
Algorithms
Curricula
Deep learning
Locomotion
Machine learning
Policies
Robots
Terrain
Workstations
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422