Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Least Squares Policy Evaluation Algorithms with Linear Function Approximation
Discrete Event Dynamic Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Least-Squares Temporal Difference Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Incremental least-squares temporal difference learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Efficient reinforcement learning using recursive least-squares methods
Journal of Artificial Intelligence Research
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This paper extends many of the recent popular policy evaluation algorithms to a generalized framework that includes least-squares temporal difference (LSTD) learning, least-squares policy evaluation (LSPE) and a variant of incremental LSTD (iLSTD). The basis of this extension is a preconditioning technique that solves a stochastic model equation. This paper also studies three significant issues of the new framework: it presents a new rule of step-size that can be computed online, provides an iterative way to apply preconditioning, and reduces the complexity of related algorithms to near that of temporal difference (TD) learning.