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Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control


[ 1 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication


Published in

Applied Sciences

Journal year: 2023 | Journal volume: vol. 13 | Journal number: iss. 16

Article type

scientific article

Publication language


  • reinforcement learning
  • continuum robots
  • control systems
  • reward functions
  • deep deterministic policy gradient

EN Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems.

Date of online publication


Pages (from - to)

9153-1 - 9153-13





Article Number: 9153

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal


Impact Factor

2.7 [List 2022]

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