Value Function Approximation in Reinforcement Learning Using the Fourier Basis

We describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Series. We empirically evaluate its properties, and 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 even though no extra experience or computation is required.

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: George Konidaris, Sarah Osentoski, Philip Thomas
• Keywords: AAAI 2011, Approximation Scheme, Intelligent Agents, Polynomial Basis, Radial Basis Functions