PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Regularization in Reinforcement Learning
Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvari and Shie Mannor
In: Multidisciplinary Symposium on Reinforcement Learning (MSRL-2009), 18 June 2009, Montreal, QC, Canada.


We develop regularized counterparts of standard Approximate Value Iteration and Approximate Policy Iteration algorithms. Our statistical analysis show that these methods have an almost optimal finite-sample sample-complexity convergence rate for value function estimation.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6123
Deposited By:Mohammad Ghavamzadeh
Deposited On:08 March 2010