Reinforcement Learning, Part 8: Feature State Construction

Reinforcement learning involves an agent learning optimal strategies in complex environments based on state rewards. This post discusses enhancing linear methods by incorporating more complex state feature interactions without leaving the linear optimization space. Methods such as polynomial features, Fourier basis, state aggregation, and radial basis functions are compared in terms of their effectiveness. Nonparametric function approximation methods are also introduced to address limitations in parametric approaches.