There has been a recent focus in reinforcement learning on addressing continuous state and action problems by optimizing parameterized policies. PI 2 is a recent example of this approach. It combines a derivation from first principles of stochastic optimal control with tools from statistical estimation theory. In this paper, we consider PI 2 as a member of the wider family of methods which share the concept of probability-weighted averaging to iteratively update parameters to optimize a cost function. We compare PI 2 to other members of the same family -Cross-Entropy Methods and CMAES -at the conceptual level and in terms of performance. The comparison suggests the derivation of a novel algorithm which we call PI 2 -CMA for "Path Integral Policy Improvement with Covariance Matrix Adaptation". PI 2 -CMA's main advantage is that it determines the magnitude of the exploration noise automatically.
This method was expanded upon by Stulp et al., who proposed Path Integral Policy Improvement with Covariance Matrix Adaptation REF .
Path Integral Policy Improvement with Covariance Matrix Adaptation
ArXiv
ArXiv
Mathematics Computer Science