Codebase to train, evaluate and analyze adversarial policies: policies attacking a fixed victim agent in a multi-agent system. See paper for more information.
The easiest way to install the code is to build the Docker image in the Dockerfile
.
This will install all necessary binary and Python dependencies. Build the image by:
$ docker build .
You can also pull a Docker image for the latest master commit from
humancompatibleai/adversarial_policies:latest
. Once you have built the image, run it by:
docker run -it --env MUJOCO_KEY=URL_TO_YOUR_MUJOCO_KEY \
humancompatibleai/adversarial_policies:latest /bin/bash # change tag if built locally
If you want to run outside of Docker (for example, for ease of development), read on.
This codebase uses Python 3.7. The main binary dependencies are MuJoCo (version 1.3.1, for
gym_compete
environments, and 2.0 for the others). You may also need to install some other
libraries, such as OpenMPI.
Create a virtual environment by running ci/build_venv.sh
. Activate it
by . ./venv/bin/activate
. Finally, run pip install -e .
to install
an editable version of this package.
Note we use Sacred for experiment configuration.
aprl.train
trains a single adversarial policy. By default it will train on SumoAnts
for
a brief period of time. You can override any of config parameters, defined in train_config
, at
the command line. For example:
# Train on Sumo Humans for 10M timesteps
python -m aprl.train with env_name=multicomp/SumoHumans-v0 total_timesteps=10000000
aprl.multi.train
trains multiple adversarial policies, using Ray (see below) for
parallelization. To replicate the results in the paper (there may be slight differences due to
randomness not captured in the seeding), run python -m aprl.multi.train with paper
. To run
the hyperparameter sweep, run python -m aprl.multi.train with hyper
.
You can find results from our training run on s3://adversarial-policies-public/multi_train/paper.
This includes TensorBoard logs, final model weights, checkpoints, and individual policy configs.
Run experiments/pull_public_s3.sh
to sync this and other data to data/aws-public/
.
aprl.score_agent
evaluates a pair of policies, for example an adversary and a victim.
It outputs the win rate for each agent and the number of ties. It can also render to the screen
or produce videos.
We similarly use aprl.multi.score
to evaluate multiple pairs of policies in parallel.
To reproduce all the evaluations used in the paper, run the following bash scripts, which call
aprl.multi.score
internally:
experiments/modelfree/baselines.sh
: fixed baselines (no adversarial policies).experiments/modelfree/attack_transfers.sh <path-to-trained-adversaries>
. To use our pre-trained policies, use the pathdata/aws-public/multi_train/paper/20190429_011349
after syncing against S3.
Most of the visualization code lives in the aprl.visualize
package. To reproduce the figures
in the paper, use paper_config
; for those in the appendix, use supplementary_config
. So:
python -m aprl.visualize.scores with paper_config # heatmaps in the paper
python -m aprl.visualize.training with supplementary_config # training curves in appendix
To re-generate all the videos, use aprl.visualize.make_videos
. We would recommend running
in Docker, in which case it will render using Xdummy
. This avoids rendering issues with many
graphics drivers.
Note you will likely need to change the default paths in the config to point at your evaluation results from the previous section, and desired output directory. For example:
python -m aprl.visualize.scores with tb_dir=<path/to/trained/models> \
transfer_path=<path/to/multi_score/output>
python -m aprl.visualize.make_videos with adversary_path=<path/to/best_adversaries.json>
The density modeling can be run by experiments/aprl/density.sh
, or with custom
configurations via aprl.density.pipeline
.
The t-SNE visualizations can be replicated with aprl.tsne.pipeline
.
Many of the experiments are computationally intensive. You can run them on a single machine, but it
might take several weeks. We use Ray to run distributed
experiments. We include example configs in src/aprl/configs/ray/
. To use aws.yaml
you
will need to, at a minimum, edit the config to use your own AMI (anything with Docker should work)
and private key. Then just run ray up <path-to-config>
and it will start a cluster. SSH into the
head node, start a shell in Docker, and then follow the above instructions. The script should
automatically detect it is part of a Ray cluster and run on the existing Ray server, rather than
starting a new one.
The codebase follows PEP8, with a 100-column maximum line width. Docstrings should be in reST.
Please run the ci/code_checks.sh
before committing. This runs several linting steps.
These are also run as a continuous integration check.
I like to use Git commit hooks to prevent bad commits from happening in the first place:
ln -s ../../ci/code_checks.sh .git/hooks/pre-commit