cff-version: 1.2.0
abstract: "<p><span style="background-color: rgb(255, 250, 234);">This collection contains all code to produce the results of </span><span style="color: rgb(51, 51, 51);">"Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee,"&nbsp;</span><em style="color: rgb(51, 51, 51);">2021 IEEE International Conference on Robotics and Automation (ICRA)</em><span style="color: rgb(51, 51, 51);">, Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. </span>This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. To the best of our knowledge, this is the first DRL-based orientation estimation method using inertial sensors combined with a magnetometer. The code is written in Python. The packages used are listed in "requirements.txt". To reproduce the code, please refer to "README.md".</p>"
authors:
  - family-names: Tang
    given-names: Yujie
  - family-names: Hu
    given-names: Liang
  - family-names: Zhou
    given-names: Zhipeng
  - family-names: Pan
    given-names: Wei
title: "code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4.v1
license: MIT
date-released: 2024-10-29