Comparative Study on Vibration Control Using Reinforcement Learning

Wanyonyi S. N., Ferhat I., KURTULUŞ D. F.

10th International Conference on Recent Advances in Air and Space Technologies, RAST 2023, İstanbul, Turkey, 7 - 09 June 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/rast57548.2023.10197999
  • City: İstanbul
  • Country: Turkey
  • Keywords: deep Q-network, proximal policy optimization, reinforcement learning, vibration control
  • Middle East Technical University Affiliated: Yes


A reinforcement learning (RL) comparative study is carried out for the vibration control of a mass-spring-damper system. First, an analysis of the implementation of two RL algorithms proximal policy optimization (PPO) and deep-Q network (DQN) for the vibration control of the system with a discrete action space is carried out. Thereafter, an investigation on the effect of defining an action space as discrete or continuous is performed. A custom RL environment is created in MATLAB, with two constructors, one defining a discrete action space and another a continuous action space. The DQN and PPO agent are then trained on the discrete environment. For the continuous action space environment, only a PPO agent is trained. The trained agents are then simulated for their respective environments and the results presented.