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

Bryon Tjanaka*
University of Southern California

Matthew C. Fontaine
University of Southern California

Aniruddha Kalkar
University of Southern California

Stefanos Nikolaidis
University of Southern California


Pre-training a diverse set of robot controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, finding diverse, high-performing controllers requires specialized hardware and extensive tuning of a large number of hyperparameters. On the other hand, the Covariance Matrix Adaptation MAP-Annealing algorithm, an evolution strategies (ES)-based quality diversity algorithm, does not have these limitations and has been shown to achieve state-of-the-art performance in standard benchmark domains. 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 very high dimensions. Our experiments show that the variants outperform ES-based baselines in benchmark robotic locomotion tasks, while being comparable with state-of-the-art deep reinforcement learning-based quality diversity algorithms.

Supplemental Video

Algorithm Overview

We propose variants of the CMA-MAE algorithm which scale to high-dimensional controllers. The variants maintain a Gaussian search distribution with mean $\bm{\phi}^*$ and approximate covariance matrix $\tilde{\bm{\Sigma}}$. Solutions $\bm{\phi}_i$ sampled from the Gaussian are evaluated and inserted into an archive, where they generate improvement feedback $\Delta_i$ based on their objective value $f(\bm{\phi}_i)$ and a threshold $t_e$ which each archive cell maintains. Finally, the Gaussian is updated with an evolution strategy (ES). Our variants differ from CMA-MAE by selecting scalable ESs, as the CMA-ES used in CMA-MAE has $\Theta(n^2)$ time complexity per sampled solution.