Technical Note: \cal Q-Learning
Machine Learning
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Least-squares policy iteration
The Journal of Machine Learning Research
Reinforcement learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Kernelized value function approximation for reinforcement learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Sparse Bayesian modeling with adaptive kernel learning
IEEE Transactions on Neural Networks
Journal of Artificial Intelligence Research
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning
IEEE Transactions on Neural Networks
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In this study we present a sparse Bayesian framework for value function approximation. The proposed method is based on the on-line construction of a dictionary of states which are collected during the exploration of the environment by the agent. A linear regression model is established for the observed partial discounted return of such dictionary states, where we employ the Relevance Vector Machine (RVM) and exploit its enhanced modeling capability due to the embedded sparsity properties. In order to speed-up the optimization procedure and allow dealing with large-scale problems, an incremental strategy is adopted. A number of experiments have been conducted on both simulated and real environments, where we took promising results in comparison with another Bayesian approach that uses Gaussian processes.