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Differentiable Quality Diversity
The official Python implementation of the NeurIPS 2021 paper

This repository replicates the code from the supplemental material for the Differentiable Quality Diversity paper.

The project contains a modified version of pyribs a quality diversity optimization library. All MEGA variants are implemented in pyribs. The GradientEmitter implements both the OG-MAP-Elites and the OMG-MEGA algorithms. The GradientImprovementEmitter implements the CMA-MEGA algorithm.

See ribs/emitters/_gradient_emitter.py and ribs/emitters/_gradient_improvement_emitter.py.

Requirements

The project builds in Anaconda.

Here are the instructions to create the conda environment:

conda env create -f experiments/environment.yml

Next install the local copy of pyribs after activating conda:

conda activate dqdexps
pip3 install -e .[all]

Pretrained Models

You can download the StyleGAN pretrained models from the StyleGAN repo. Place the .pt file in the folder experiments/lsi_clip.

CLIP automatically installs with the conda environment.

Running Experiments

For each experiment you pick an identifier for the algorithm you want to run.

Quality Diversity Algorithm Identifier
MAP-Elites map_elites
MAP-Elites (line) map_elites_line
CMA-ME cma_me_imp
OG-MAP-Elites og_map_elites
OG-MAP-Elites (line) og_map_elites_line
OMG-MEGA omg_mega
CMA-MEGA cma_mega
CMA-MEGA (Adam) cma_mega_adam

Linear Projection (sphere)

To run an experiment with MAP-Elites:

conda activate dqdexps
cd experiments/lin_proj

python3 lin_proj.py map_elites --objective sphere

To run a different algorithm replace map_elites with another identifier from the above table.

For additional options see:

python3 lin_proj.py --help

Linear Projection (Rastrigin)

To run an experiment with MAP-Elites:

conda activate dqdexps
cd experiments/lin_proj

python3 lin_proj.py map_elites --objective Rastrigin

To run a different algorithm replace map_elites with another identifier from the above table.

For additional options see:

python3 lin_proj.py --help

Arm Repertoire

To run an experiment with MAP-Elites:

conda activate dqdexps
cd experiments/arm

python3 arm.py map_elites

To run a different algorithm replace map_elites with another identifier from the above table.

For additional options see:

python3 arm.py --help

Latent Space Illumination (LSI)

To run an experiment with MAP-Elites:

conda activate dqdexps
cd experiments/lsi_clip

python3 lsi.py map_elites

To run a different algorithm replace map_elites with another identifier from the above table.

For additional options see:

python3 lsi.py --help

Results

The following tables contain the reported results from the DQD paper.

Linear Projection (sphere)

Quality Diversity Algorithms QD-score Coverage
MAP-Elites 1.04 1.17%
MAP-Elites (line) 12.21 14.32%
CMA-ME 1.08 1.21%
OG-MAP-Elites 1.52 1.67%
OMG-MEGA 71.58 92.09%
CMA-MEGA 75.29 100.00%
CMA-MEGA (Adam) 75.3 100.00%

Linear Projection (Rastrigin)

Quality Diversity Algorithms QD-score Coverage
MAP-Elites 1.18 1.72%
MAP-Elites (line) 8.12 11.79%
CMA-ME 1.21 1.76%
OG-MAP-Elites 0.83 1.26%
OMG-MEGA 55.90 77.00%
CMA-MEGA 62.54 100.00%
CMA-MEGA (Adam) 62.58 100.00%

Arm Repertoire

Quality Diversity Algorithms QD-score Coverage
MAP-Elites 1.97 8.06%
MAP-Elites (line) 33.51 35.79%
CMA-ME 55.98 56.95%
OG-MAP-Elites 57.17 58.08%
OMG-MEGA 44.12 44.13%
CMA-MEGA 74.18 74.18%
CMA-MEGA (Adam) 73.82 73.82%

Latent Space Illumination (LSI)

Quality Diversity Algorithms QD-score Coverage
MAP-Elites 13.88 23.15%
MAP-Elites (line) 16.54 25.73%
CMA-ME 18.96 26.18%
CMA-MEGA 5.36 8.61%
CMA-MEGA (Adam) 21.82 30.73%

See the paper and supplementary materials for full data and standard error bars.

OG-MAP-Elites Ablations

To run the independent operator ablation experiments for OG-MAP-Elites, pick from the following identifiers

Quality Diversity Algorithm Identifier
OG-MAP-Elites og_map_elites
OG-MAP-Elites (line) og_map_elites_line
OG-MAP-Elites† og_map_elites_ind
OG-MAP-Elites (line)† og_map_elites_line_ind

License

pyribs and this project are both released under the MIT License.

pyribs MIT License