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Policy evaluation is a key process in reinforcement learning. It assesses agiven policy using estimation of the corresponding value function. When using aparameterized function to approximate the value, it is common to optimize theset of parameters by minimizing the sum of squared Bellman Temporal Differenceserrors. However, this approach ignores certain distributional properties ofboth the errors and value parameters. Taking these distributions into accountin the optimization process can provide useful information on the amount ofconfidence in value estimation. In this work we propose to optimize the valueby minimizing a regularized objective function which forms a trust region overits parameters. We present a novel optimization method, the Kalman Optimizationfor Value Approximation (KOVA), based on the Extended Kalman Filter. KOVAminimizes the regularized objective function by adopting a Bayesian perspectiveover both the value parameters and noisy observed returns. This distributionalproperty provides information on parameter uncertainty in addition to valueestimates. We provide theoretical results of our approach and analyze theperformance of our proposed optimizer on domains with large state and actionspaces.

Trust Region Value Optimization using Kalman Filtering