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arXiv · 2210.11801

Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search

Elias Hanna·Alex Coninx·Stéphane Doncieux
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Abstract

This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model. Dynamics models approximate the true transition function of a given task, in order to perform policy search directly on the model rather than on the costly real system. This study aims to determine how to bootstrap a model as efficiently as possible, by comparing initialization methods employed in two different policy search frameworks in the literature. The study focuses on the model performance under the episode-based framework of Evolutionary methods using probabilistic ensembles. Experimental results show that various task-dependant factors can be detrimental to each method, suggesting to explore hybrid approaches.

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Elias Hanna, Alex Coninx, Stéphane Doncieux. 2022-10-21. Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search. https://arxiv.org/abs/2210.11801

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