Scaling Covariance Matrix Adaptation
MAP-Annealing to High-Dimensional
Controllers

Deep RL Workshop at NeurIPS 2022 — 9 December 2022

Bryon Tjanaka
University of Southern California
tjanaka@usc.edu

Matthew C. Fontaine
University of Southern California
mfontain@usc.edu

Aniruddha Kalkar
University of Southern California
kalkar@usc.edu

Stefanos Nikolaidis
University of Southern California
nikolaid@usc.edu

	Low-dimensional QD	(High-dimensional) QD-RL
CMA-MAE	✓	⨯

Deep RL-based QD	Scalable CMA-MAE
✓ SOTA	✓ SOTA on 3/4 tasks
⨯ Requires GPU	✓ Runs on CPU
⨯ Neural network training	✓ No training
⨯ Many hyperparameters	✓ Minimal hyperparameters

Scaling Covariance Matrix Adaptation MAP-Annealing to High-Dimensional Controllers Deep RL Workshop at NeurIPS 2022 — 9 December 2022 Bryon Tjanaka University of Southern California tjanaka@usc.edu Matthew C. Fontaine University of Southern California mfontain@usc.edu Aniruddha Kalkar University of Southern California kalkar@usc.edu Stefanos Nikolaidis University of Southern California nikolaid@usc.edu Website scalingcmamae.github.io

Scaling Covariance Matrix Adaptation
MAP-Annealing to High-Dimensional
Controllers

Quality Diversity for Reinforcement Learning (QD-RL)

Covariance Matrix Adaptation MAP-Annealing (CMA-MAE)

Experiments

Scaling Covariance Matrix Adaptation
MAP-Annealing to High-Dimensional
Controllers

Supplemental

Scaling Covariance Matrix AdaptationMAP-Annealing to High-DimensionalControllers

Scaling Covariance Matrix Adaptation
MAP-Annealing to High-Dimensional
Controllers