Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Reinforcement learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Analyzing feature generation for value-function approximation
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Control Techniques for Complex Networks
Control Techniques for Complex Networks
Least Squares SVM for Least Squares TD Learning
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Capacity of reproducing kernel spaces in learning theory
IEEE Transactions on Information Theory
Approximate dynamic programming with a fuzzy parameterization
Automatica (Journal of IFAC)
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Reinforcement learning with linear and non-linear function approximation has been studied extensively in the last decade. However, as opposed to other fields of machine learning such as supervised learning, the effect of finite sample has not been thoroughly addressed within the reinforcement learning framework. In this paper we propose to use L2 regularization to control the complexity of the value function in reinforcement learning and planning problems. We consider the Regularized Fitted Q-Iteration algorithm and provide generalization bounds that account for small sample sizes. Finally, a realistic visual-servoing problem is used to illustrate the benefits of using the regularization procedure.