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

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