Value Function Approximation in Reinforcement Learning Using the Fourier Basis 论文
2011Proceedings of the AAAI Conference on Artificial Intelligence引用 278
Reinforcement Learning in RoboticsEvolutionary Algorithms and ApplicationsVLSI and FPGA Design Techniques
摘要
We describe the Fourier basis, a linear value function approximation scheme based on the Fourier series. We empirically demonstrate that it performs well compared to radial basis functions and the polynomial basis, the two most popular fixed bases for linear value function approximation, and is competitive with learned proto-value functions.