marcelomata/adversarial-policies

Find best-response to a fixed policy in multi-agent RL

Codebase to train, evaluate and analyze adversarial policies: policies attacking a fixed victim agent in a multi-agent system. See paper for more information.

Installation

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 --build-arg MUJOCO_KEY=URL_TO_YOUR_MUJOCO_KEY .

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 modelfree and 1.5.0 for aprl). You may also need to install some other libraries, such as OpenMPI.

There are two different parts of the codebase, src/aprl and src/modelfree with mutually exclusive requirements (due to different Gym and MuJoCo versions). You can create a virtual environment for each by running ci/build_venv.sh with either aprl or modelfree. Finally, inside the relevant virtual environment run pip install -e . to install an editable version of the package.

Reproducing Results

For all of these experiments, you should be in the modelfree virtual environment:

source ./modelfreevenv/bin/activate

We use Sacred for experiment configuration.

Training adversarial policies

modelfree.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 modelfree.train with env_name=multicomp/SumoHumans-v0 total_timesteps=10000000

modelfree.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 modelfree.multi.train with paper. To run the hyperparameter sweep, run python -m modelfree.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/.

Evaluating adversarial policies

modelfree.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 modelfree.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 modelfree.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 path data/aws-public/multi_train/paper/20190429_011349 after syncing against S3.

Visualizing Results

Most of the visualization code lives in the modelfree.visualize package. To reproduce the figures in the paper, use paper_config; for those in the appendix, use supplementary_config. So:

  python -m modelfree.visualize.scores with paper_config  # heatmaps in the paper
  python -m modelfree.visualize.training with supplementary_config  # training curves in appendix

To re-generate all the videos, use modelfree.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 modelfree.visualize.scores with tb_dir=<path/to/trained/models> \
                                          transfer_path=<path/to/multi_score/output>
python -m modelfree.visualize.make_videos with adversary_path=<path/to/best_adversaries.json>

Additional Analysis

The density modeling can be run by experiments/modelfree/density.sh, or with custom configurations via modelfree.density.pipeline.

The t-SNE visualizations can be replicated with modelfree.tsne.pipeline.

Using Ray

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/modelfree/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.

Contributions

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