This repository contains the python codes of the paper
- Panda, J., Chopra, M., Matsagar, V., & Chakraborty, S. (2023). An iterative gradient descent-based reinforcement learning policy for active control of structural vibrations. Computers & Structures, Accepted, in press. [Article]
A short description of the files is provided below for ease of reading. Folder: QCModel (Continuous time)
RLAlgorithm_QCmodel_P.ipynb: This code is for RL-based control algorithm in proportional (P) to state feedback case (Case study I: Quarter car model).RLAlgorithm_QCmodel_PI.ipynb: This code is for RL-based control algorithm in proportional-integral (PI) to state-output feedback (Case study I: Quarter car model). Folder: 8Story (Discrete time)RLAlgorithm_8Story_P.ipynb: This code is for RL-based control algorithm in proportional (P) to state feedback case (Case study II: 8-story benchmark building).RLAlgorithm_8Story_PI.ipynb: This code is for RL-based control algorithm in proportional-integral (PI) to state-output feedback (Case study II: 8-story benchmark building).Validation_8Story_P&PI.ipynb: This code is for the robust performance of trained RL controllers in P and PI feedback (Section 5.2.2, Figure 11). Considered SAC ground motions to validate the robust performance of the trained controllers are placed in the folder '8Story' along with the state-space matrices and updated P and PI controller gains. In case, the location of the mentioned data is changed, the correct path should be given.
The following packages are required to be installed to run the above codes:
If you use any part of our codes, please cite us at,
@article{Panda2023RL, title={ An iterative gradient descent-based reinforcement learning policy for active control of structural vibrations}, author={Panda, J. and Chopra, M. and Matsagar, V. and Chakraborty, S}, journal={Computers & Structures}, volume={}, pages={}, year={2023}, publisher={Elsevier} }