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.