Optimization of Energy Policies Using Direct Value Search

Direct Policy Search is a widely used tool for reinforcement learning; however, it is usually not suitable for handling high-dimensional constrained action spaces such as those arising in power system control (unit commitmen problems). We propose Direct Value Search, an hybridization of DPS with Bellman decomposition techniques. We prove runtime properties, and apply the results to an energy management problem.