• Zihan Ding* - Princeton University (zhding[at]
  • Hao Dong - Peking University


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


Codes for contents in this chapter are available here.


To cite this book, please use this bibtex entry:

 title={Robot Learning in Simulation},
 author={Zihan Ding, Hao Dong},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},

If you find any typos or have suggestions for improving the book, do not hesitate to contact with the corresponding author (name with *).