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Scenario co-evolution for reinforcement learning on a grid ..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference,

Gabor, Thomas ; Sedlmeier, Andreas ; Kiermeier, Marie... - p. 898-906 , 2019

Link: https://dl.acm.org/doi/10.1145/3321707.3321831

RT 
T1 <cTitle>Proceedings of the Genetic and Evolutionary Computation Conference</cTitle>: T1 <Title>Scenario co-evolution for reinforcement learning on a grid world smart factory domain</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3321831&Exemplar=1&LAN=DE
				
A1 Gabor, Thomas
A1 Sedlmeier, Andreas
A1 Kiermeier, Marie
A1 Phan, Thomy
A1 Henrich, Marcel
A1 Pichlmair, Monika
A1 Kempter, Bernhard
A1 Klein, Cornel
A1 Sauer, Horst
A1 AG, Reiner SchmidSiemens
A1 Wieghardt, Jan
PB ACM
YR 2019
K1 adversarial learning
K1 automatic test generation
K1 coevolution
K1 evolutionary algorithms
K1 reinforcement learning
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Reinforcement learning
K1 Adversarial learning
K1 Machine learning approaches
K1 Neural networks
K1 Bio-inspired approaches
K1 Genetic algorithms
K1 Artificial intelligence
K1 Distributed artificial intelligence
K1 Mobile agents
K1 Planning and scheduling
K1 Robotic planning
K1 Instance-based learning
K1 Generative and developmental approaches
SP 898
OP 906
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3321707.3321831
DO https://dl.acm.org/doi/10.1145/3321707.3321831
SF ELIB - SuUB Bremen

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