Imperial College London


Faculty of EngineeringDepartment of Computing

Reader in Machine Learning and Robotics



+44 (0)20 7594 8204a.cully Website




354ACE ExtensionSouth Kensington Campus






Lim BWT, Flageat M, Cully A, 2023, Efficient exploration using model-based quality-diversity with gradients, Conference on Artificial Life, MIT Press, Pages:1-10

Boige R, Richard G, Dona J, et al., 2023, Gradient-informed quality diversity for the illumination of discrete spaces, Pages:119-128

Flageat M, Grillotti L, Cully A, 2023, Benchmark Tasks for Quality-Diversity Applied to Uncertain Domains, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, ACM

Faldor M, Chalumeau F, Flageat M, et al., 2023, MAP-elites with descriptor-conditioned gradients and archive distillation into a single policy, The Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Pages:138-146

Janmohamed H, Pierrot T, Cully A, 2023, Improving the data efficiency of multi-objective quality-diversity through gradient assistance and crowding exploration, GECCO 2023, ACM, Pages:165-173

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