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Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation

Le Goff, Léni K.; Buchanan, Edgar; Hart, Emma; Eiben, Agoston E.; Li, Wei; De Carlo, Matteo; Hale, Matthew F.; Angus, Mike; Woolley, Robert; Timmis, Jon; Winfield, Alan; Tyrrell, Andrew M.

Authors

Edgar Buchanan

Agoston E. Eiben

Wei Li

Matteo De Carlo

Matthew F. Hale

Mike Angus

Robert Woolley

Jon Timmis

Alan Winfield

Andrew M. Tyrrell



Abstract

In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a 'newborn' robot in order to better align it to its newly generated morphology. This can be accomplished via a learning mechanism applied to each individual robot: for practical reasons, such a mechanism should be both sample and time-efficient. In this paper, We investigate two ways to improve the sample and time efficiency of the well-known learner CMA-ES on navigation tasks. The first approach combines CMA-ES with Novelty Search, and includes an adaptive restart mechanism with increasing population size. The second bootstraps CMA-ES using Bayesian Optimisation, known for its sample efficiency. Results using two robots built with the ARE project's modules and four environments show that novelty reduces the number of samples needed to converge, as does the custom restart mechanism; the latter also has better sample and time efficiency than the hybridised Bayesian/Evolutionary method.

Citation

Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., …Tyrrell, A. M. (2020). Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation. In ALIFE 2020: The 2020 Conference on Artificial Life (432-440). https://doi.org/10.1162/isal_a_00299

Conference Name ALife 2020
Conference Location Online
Start Date Jul 13, 2020
End Date Jul 18, 2020
Acceptance Date Jun 1, 2020
Online Publication Date Jul 14, 2020
Publication Date 2020-07
Deposit Date Jul 15, 2020
Publicly Available Date Jul 15, 2020
Publisher MIT Press
Pages 432-440
Book Title ALIFE 2020: The 2020 Conference on Artificial Life
DOI https://doi.org/10.1162/isal_a_00299
Public URL http://researchrepository.napier.ac.uk/Output/2675888
Publisher URL https://www.mitpressjournals.org/toc/isal/32

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