Tunnel ventilation control using reinforcement learning methodology

Baeksuk Chu, Dongnam Kim, Daehie Hong, Jooyoung Park, Jin Taek Chung, Tae Hyung Kim

Research output: Contribution to journalArticlepeer-review


The main purpose of tunnel ventilation s stem is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

Original languageEnglish
Pages (from-to)1003-1010
Number of pages8
JournalJSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing
Issue number4
Publication statusPublished - 2007 Jun 15


  • Actor-critic architecture
  • Gradient-following algorithm
  • Reinforcement learning (RL)
  • Tunnel ventilation control

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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