Research Area:  Machine Learning

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.

Keywords:  
Multi-Goal Reinforcement Learning
Robotics Environments
Machine Learning
Deep Learning

Author(s) Name:  Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, Wojciech Zaremba

Publisher name:  arXiv:1802.09464

DOI:  https://doi.org/10.48550/arXiv.1802.09464

Volume Information:  

Paper Link:   https://arxiv.org/abs/1802.09464