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There has been a recent focus in reinforcement learning on addressingcontinuous state and action problems by optimizing parameterized policies. PI2is a recent example of this approach. It combines a derivation from firstprinciples of stochastic optimal control with tools from statistical estimationtheory. In this paper, we consider PI2 as a member of the wider family ofmethods which share the concept of probability-weighted averaging toiteratively update parameters to optimize a cost function. We compare PI2 toother members of the same family - Cross-Entropy Methods and CMAES - at theconceptual level and in terms of performance. The comparison suggests thederivation of a novel algorithm which we call PI2-CMA for "Path Integral PolicyImprovement with Covariance Matrix Adaptation". PI2-CMA's main advantage isthat it determines the magnitude of the exploration noise automatically.

Path Integral Policy Improvement with Covariance Matrix Adaptation