Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
Fabio Amadio,
Alberto Dalla Libera,
Riccardo Antonello
et al.
Abstract:In this paper, we present a Model-Based Reinforcement Learning algorithm named 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 … Show more
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