In order to practically implement the door opening task, a policy ought to be robust to a wide distribution of door types and environment settings. Reinforcement Learning (RL) with Domain Randomization (DR) is a promising technique to enforce policy generalization, however, there are only a few accessible training environments that are inherently designed to train agents in domain randomized environments. We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy. We intend for our environment to lie at the intersection of domain transfer, practical tasks, and realism. We also provide baseline Proximal Policy Optimization and Soft Actor-Critic implementations, which achieves success rates between 0% up to 95% for opening various types of doors in this environment. Moreover, the real-world transfer experiment shows the trained policy is able to work in the real world. Environment kit available here: https://github.com/PSVL/DoorGym/
@article{arxiv.1908.01887,
title = {DoorGym: A Scalable Door Opening Environment And Baseline Agent},
author = {Yusuke Urakami and Alec Hodgkinson and Casey Carlin and Randall Leu and Luca Rigazio and Pieter Abbeel},
journal= {arXiv preprint arXiv:1908.01887},
year = {2022}
}
Comments
Accepted to NeurIPS2019 Deep Reinforcement Learning Workshop. Full version