Hierarchical planning and stochastic optimization algorithms with applications to self-driving vehicles and finance

This work discusses hierarchical planning and stochastic optimizationalgorithms with applications to self-driving vehiclesand quantitative finance. A diverse set of mathematicaltools is considered, ranging from model predictive control(MPC), in both the deterministic and stochastic setting, tovarious vehicle routing problems, and reinforcement learningusing neural networks for function approximation. Theapplications discussed include single- and multi-automatedvehicle motion planning, agricultural in- and out-field logisticsplanning, as well as dynamic option hedging.