Position Control of a Mobile Robot through Deep Reinforcement Learning

Francisco Quiroga, Gabriel Hermosilla, Gonzalo Farias, Ernesto Fabregas, Guelis Montenegro

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This article proposes the use of reinforcement learning (RL) algorithms to control the position of a simulated Kephera IV mobile robot in a virtual environment. The simulated environment uses the OpenAI Gym library in conjunction with CoppeliaSim, a 3D simulation platform, to perform the experiments and control the position of the robot. The RL agents used correspond to the deep deterministic policy gradient (DDPG) and deep Q network (DQN), and their results are compared with two control algorithms called Villela and IPC. The results obtained from the experiments in environments with and without obstacles show that DDPG and DQN manage to learn and infer the best actions in the environment, allowing us to effectively perform the position control of different target points and obtain the best results based on different metrics and indices.

Original languageEnglish
Article number7194
JournalApplied Sciences (Switzerland)
Issue number14
StatePublished - Jul 2022


  • kephera
  • position control
  • reinforcement learning
  • simulated environment


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