Regularized Fitted Q-Iteration: Application to Planning

We consider planning in a Markovian decision problem, i.e., the problem of finding a good policy given access to a generative model of the environment. We propose to use fitted Q-iteration with penalized (or regularized) least-squares regression as the regression subroutine to address the problem of controlling model-complexity. The algorithm is presented in detail for the case when the function space is a reproducingkernel Hilbert space underlying a user-chosen kernel function. We derive bounds on the quality of the solution and argue that data-dependent penalties can lead to almost optimal performance. A simple example is used to illustrate the benefits of using a penalized procedure.

• Type: Conference
• Year: 2008
• Where: EWRL
• Authors: Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor
• Keywords: EWRL 2008, Machine Learning, Markovian Decision Problem, Policy Given Access, User-chosen Kernel Function