Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application. (arXiv:2101.12115v4 [cs.LG] UPDATED)

In this paper, we present a Model-Based Reinforcement Learning (MBRL)
algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol}
(MC-PILCO). The algorithm relies on Gaussian Processes (GPs) to model the
system dynamics and on a Monte Carlo approach to estimate the policy gradient.
This defines a framework in which we ablate the choice of the following
components: (i) the selection of the cost function, (ii) the optimization of
policies using dropout, (iii) an improved data efficiency through the use of …

application arxiv gradient policy search systems