STL-Synthesis-among-Uncontrollable-Agents

We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over the system and uncontrollable stochastic agents. Here is the paper link.

Feel free to reproduce our simulations. To facilitate this, we have provided all the original and intermediate data sets. This enables readers to replicate any segment of our simulations with ease. There's no need to execute the code from Step0—you can begin from any step, as each one is designed to be independent of the others in our codes.

Dependencies

We use the following packages

tensorflow                2.16.1
scip                      8.0.4                
scipy                     1.10.1

Acknowledgement

We would like to thank our friends Junjie, Jiageng and Johnny for their great help in the learning implementation!