Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Machine Learning
2018-03-13 v2 Artificial Intelligence
Robotics
Abstract
The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent is told what to do using an additional input. The second part of the paper presents a set of concrete research ideas for improving RL algorithms, most of which are related to Multi-Goal RL and Hindsight Experience Replay.
Cite
@article{arxiv.1802.09464,
title = {Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research},
author = {Matthias Plappert and Marcin Andrychowicz and Alex Ray and Bob McGrew and Bowen Baker and Glenn Powell and Jonas Schneider and Josh Tobin and Maciek Chociej and Peter Welinder and Vikash Kumar and Wojciech Zaremba},
journal= {arXiv preprint arXiv:1802.09464},
year = {2018}
}