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

Autor:	Hanna, Elias, Coninx, Alex, Doncieux, Stéphane
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICML 2022 Workshop Adaptive Experimental Design and Active Learning in the Real World
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.11801 Zobrazit plný text záznamu View this record from Arxiv