All Outputs (2)

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning (2022)
Presentation / Conference Contribution
Flageat, M., Lim, B., Grillotti, L., Allard, M., Smith, S. C., & Cully, A. (2022, July). Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. Paper presented at Gecco 2022, Boston, Massachusetts

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments, behavioral descriptors, and fitness. We specify different benchmarks... Read More about Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning.

Hierarchical quality-diversity for online damage recovery (2022)
Presentation / Conference Contribution
Allard, M., Smith, S. C., Chatzilygeroudis, K., & Cully, A. (2022). Hierarchical quality-diversity for online damage recovery. In J. E. Fieldsend (Ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference (58-67). https://doi.org

Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages... Read More about Hierarchical quality-diversity for online damage recovery.