Adaptive filter theory
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Technical Update: Least-Squares Temporal Difference Learning
Machine Learning
Variable Resolution Discretization in Optimal Control
Machine Learning
Least-squares policy iteration
The Journal of Machine Learning Research
Incremental Learning of Linear Model Trees
Machine Learning
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning
Discrete Event Dynamic Systems
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Incremental least-squares temporal difference learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Efficient reinforcement learning using recursive least-squares methods
Journal of Artificial Intelligence Research
Recent Advances in Reinforcement Learning
Journal of Artificial Intelligence Research
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Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to converge, but often do so slowly or are computationally expensive. In this paper, we propose to improve the convergence speed of piecewise linear function approximation by tracking the dynamics of the value function with the Kalman filter using a random-walk model. We cast this as a general framework in which we implement the TD, Q-Learning and MAXQ algorithms for different domains, and report empirical results demonstrating improved learning speed over previous methods.