Authors

• Zihan Ding* - Princeton University (zhding[at]mail.ustc.edu.cn)
• Hao Dong - Peking University

Abstract

This chapter introduces a hands-on project for robot learning in simulation, including the process of setting up a task with a robot arm for objects grasping in CoppeliaSim and the deep reinforcement learning solution with soft actor-critic algorithm. The effects of different reward functions are also shown in the experimental sections, which testifies the importance of auxiliary dense rewards for solving a hard-to-explore task like the robot grasping ones. Brief discussions on robot learning applications, sim-to-real transfer, other robot learning projects and simulators are also provided at the end of this chapter.

Keywords: robot learning, deep reinforcement learning, simulation, dense reward, parallel training, soft actor-critic, domain randomization

Content

中文版PDF

Code

Codes for contents in this chapter are available here.

Citation

To cite this book, please use this bibtex entry:

@incollection{deepRL-chapter16-2020,
 title={Robot Learning in Simulation},
 chapter={16},
 author={Zihan Ding, Hao Dong},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},
 pages={417-442},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}

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