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arXiv · 1309.6868

Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs

Charles Tripp·Ross D. Shachter
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Abstract

We seek to learn an effective policy for a Markov Decision Process (MDP) with continuous states via Q-Learning. Given a set of basis functions over state action pairs we search for a corresponding set of linear weights that minimizes the mean Bellman residual. Our algorithm uses a Kalman filter model to estimate those weights and we have developed a simpler approximate Kalman filter model that outperforms the current state of the art projected TD-Learning methods on several standard benchmark problems.

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Charles Tripp, Ross D. Shachter. 2013-09-26. Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs. https://arxiv.org/abs/1309.6868

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