Multidimensional Triangulation and Interpolation for Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Safe Model-based Reinforcement Learning with Stability Guarantees (2017, methodology): “Specifically, we use a piecewiselinear triangulation of the state-space as to approximate Jπθ , see [39].”
Variable Resolution Discretization in Optimal Control (2002, methodology): “The approach of using Kuhn triangulations to interpolate the value function has been introduced to the reinforcement learning literature by Davies (1997).”
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate (2018, unknown): “This function is simplicial interpolation [20, 27].”
Planning algorithms (2006, methodology): “If barycentric subdivision is used to decompose the cube using the midpoints of all faces, then the point-location problem can be solved in O(n lg n) time [265, 610, 724], which is an improvement over the O(2) scheme described above.”
Intelligent Control of Closed-Loop Sedation in Simulated ICU Patients (2004, background): “Davies (1997) observed good performance with linearly interpolated value functions, though the state space was coarsely partitioned.”
Generalized Prioritized Sweeping (1997, background): “, see [1]), we claim that classic PS is ill-suited in this case.”