The problem of optimal unsignalized intersection management for continual streams of randomly arriving robots is considered. This problem involves repeatedly solving different instances of a mixed integer program, for which the computation time using a naive optimization algorithm scales exponentially with the number of robots and lanes. Hence, such an approach is not suitable for real-time implementation. On the other hand the use of end-to-end RL with formal safety guarantees for a fast moving multi-agent system is still a open area of research. This work proposes a hybrid framework that use Deep RL methods for learning a combinatorial problem and MPC for trajectory plannig. This way we can guarantee a formal safety for collision avoidance and a siginificant reduction in computational time.
- clone the repository
git clone https://github.com/Mowbray-R-V/DRL-based-MPC-for-safety-critical-planning.git
- Create a conda environment and install
conda env create -f environment.yml
conda activate sample
run run_tr-te.sh