AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Mode-Finding for Mixtures of Gaussian Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kalman filter control embedded into the reinforcement learning framework
Neural Computation
PAC model-free reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel rewards regression: an information efficient batch policy iteration approach
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Proceedings of the 24th international conference on Machine learning
Journal of Artificial Intelligence Research
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A wide variety of function approximation schemes have been applied to reinforcement learning. However, Bayesian filtering approaches, which have been shown efficient in other fields such as neural network training, have been little studied. We propose a general Bayesian filtering framework for reinforcement learning, as well as a specific implementation based on sigma point Kalman filtering and kernel machines. This allows us to derive an efficient off-policy model-free approximate temporal differences algorithm which will be demonstrated on two simple benchmarks.