10 citations · 18 across the 4 of their papers we have counts for
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cs.NE2022
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Bryan Lim, Manon Flageat, Antoine Cully
Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches h…
cs.NE2022★ 8 cited
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
Manon Flageat, Bryan Lim, Luca Grillotti +3
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…