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Learning behaviour-performance maps with meta-evolution

Learning behaviour-performance maps with meta-evolution
Learning behaviour-performance maps with meta-evolution

The MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments, since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covariance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system, we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot.

Behavioural diversity, Damage recovery, Evolutionary robotics, Meta-learning, Quality-diversity algorithms
49-57
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Mouret, Jean-Baptiste
a837dbc0-1852-4e6f-93d8-41d927305eaf
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Mouret, Jean-Baptiste
a837dbc0-1852-4e6f-93d8-41d927305eaf
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4

Bossens, David, Mouret, Jean-Baptiste and Tarapore, Danesh (2020) Learning behaviour-performance maps with meta-evolution. In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference. pp. 49-57 . (doi:10.1145/3377930.3390181).

Record type: Conference or Workshop Item (Paper)

Abstract

The MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments, since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covariance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system, we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot.

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More information

Published date: 25 June 2020
Additional Information: Funding Information: This work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) under the New Investigator Award grant (EP/R030073/1), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (GA no. 637972, project “ResiBots”) and the Lifelong Learning Machines program (L2M) from DARPA/MTO under Contract No. FA8750-18-C-0103. Publisher Copyright: © 2020 ACM.
Keywords: Behavioural diversity, Damage recovery, Evolutionary robotics, Meta-learning, Quality-diversity algorithms

Identifiers

Local EPrints ID: 441277
URI: http://eprints.soton.ac.uk/id/eprint/441277
PURE UUID: af83dadc-2bd1-4cc9-8f89-62282b75f6c7
ORCID for David Bossens: ORCID iD orcid.org/0000-0003-1924-5756
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 08 Jun 2020 16:31
Last modified: 05 Oct 2022 02:00

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Contributors

Author: David Bossens ORCID iD
Author: Jean-Baptiste Mouret
Author: Danesh Tarapore ORCID iD

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