Recursive Sparse Pseudo-input Gaussian Process SARSA

2018-11-17 · John Martin, Brendan Englot

The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its predictive moments. This extension promotes greater memory efficiency, since previous computations can be reused and, interestingly, it provides a technique for updating value estimates on a multiple timescales

📄 PDF Abstract BibTeX arXiv:1811.07201

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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