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

This work discusses hierarchical planning and stochastic optimization algorithms with applications to self-driving vehicles and quantitative finance. A diverse set of mathematical tools is considered, ranging from model predictive control (MPC), in both the deterministic and stochastic setting, to various vehicle routing problems, and reinforcement learning using neural networks for function approximation. The applications discussed include single- and multi-automated vehicle motion planning, agricultural in- and out-field logistics planning, as well as dynamic option hedging.