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A key challenge for gradient based optimization methods in model-freereinforcement learning is to develop an approach that is sample efficient andhas low variance. In this work, we apply Kronecker-factored curvatureestimation technique (KFAC) to a recently proposed gradient estimator forcontrol variate optimization, RELAX, to increase the sample efficiency of usingthis gradient estimation method in reinforcement learning. The performance ofthe proposed method is demonstrated on a synthetic problem and a set of threediscrete control task Atari games.

KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning