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Pre-training a diverse set of neural network controllers in simulation has
enabled robots to adapt online to damage in robot locomotion tasks. However,
finding diverse, high-performing controllers requires expensive network
training and extensive tuning of a large number of hyperparameters. On the
other hand, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), an evolution
strategies (ES)-based quality diversity algorithm, does not have these
limitations and has achieved state-of-the-art performance on standard QD
benchmarks. However, CMA-MAE cannot scale to modern neural network controllers
due to its quadratic complexity. We leverage efficient approximation methods in
ES to propose three new CMA-MAE variants that scale to high dimensions. Our
experiments show that the variants outperform ES-based baselines in benchmark
robotic locomotion tasks, while being comparable with or exceeding
state-of-the-art deep reinforcement learning-based quality diversity
algorithms.

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