ShuhuaGao / bcn_opt_dc

Optimal Control of Boolean Control Networks with Discounted Cost: An Efficient Approach based on Deterministic Markov Decision Process

Optimal Control of Boolean Control Networks with Discounted Cost

Code accompanying the paper: Gao, Shuhua, Cheng Xiang, and Tong Heng Lee. "Optimal Control of Boolean Control Networks with Discounted Cost: An Efficient Approach based on Deterministic Markov Decision Process." 2020 IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 2020.

Requirement

Python 3.6 or higher.

Packages:

• networkx required for our graphical approach
• numpy required for the algebraic approach

How to run

• Download or clone this repository to your local computer.

• In the command line (such as cmd, PowerShell on Windows or terminal on Ubuntu), go into the code folder

• To run an example, say ara_operon.py, just type python ./ara_operon.py. (Of course, you can also use any IDEs like PyCharm or Visual Studio Code as you want.)