TY - GEN
T1 - General controllers evolved through grammatical evolution with a divergent search
AU - Naredo, Enrique
AU - Ryan, Conor
AU - Guevara, Ivan
AU - Margaria, Tiziana
AU - Urbano, Paulo
AU - Trujillo, Leonardo
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/7/8
Y1 - 2020/7/8
N2 - In this work, we analyse the performance of Novelty Search (NS) in a set of generalization experiments in a navigation task with Grammatical Evolution. Agents are trained on a single, simple environment, and tested on a selection of related, increasingly more difficult environments. We show that agents discovered with NS, although using a tiny number (six) of training samples, successfully generalise to these more difficult environments.
AB - In this work, we analyse the performance of Novelty Search (NS) in a set of generalization experiments in a navigation task with Grammatical Evolution. Agents are trained on a single, simple environment, and tested on a selection of related, increasingly more difficult environments. We show that agents discovered with NS, although using a tiny number (six) of training samples, successfully generalise to these more difficult environments.
KW - Generalization
KW - Grammatical evolution
KW - Novelty search
UR - http://www.scopus.com/inward/record.url?scp=85089736669&partnerID=8YFLogxK
U2 - 10.1145/3377929.3390059
DO - 10.1145/3377929.3390059
M3 - Conference contribution
AN - SCOPUS:85089736669
T3 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
SP - 243
EP - 244
BT - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Y2 - 8 July 2020 through 12 July 2020
ER -