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arXiv · 2210.02622

Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing

Bryon Tjanaka·Matthew C. Fontaine·David H. Lee·Aniruddha Kalkar·Stefanos Nikolaidis
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Abstract

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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Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Aniruddha Kalkar, Stefanos Nikolaidis. 2022-10-06. Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing. https://arxiv.org/abs/2210.02622

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