Code for Adversarial Cheap Talk (ICML 2023)
Lu, Chris, Timon Willi, Alistair Letcher, and Jakob Foerster. "Adversarial Cheap Talk."
Due to the rapid development of JAX's ecosystem it can be difficult for users to precisely set up the environment. We highly recommend instead using the PureJaxRL repository to perform related research. We plan to upload a clean re-implementation of this work there. This repository is for reproducing the original results in the paper.
PureJaxRL is similar to this repository in that it contains end-to-end Jax-vectorised PPO implementations. However, it differs from this repository in many ways -- it uses newer libraries that did not exist at the time that the bulk of this research was performed. In particular, Gymnax is extremely convenient because we originally implemented the Jax-based environments just for this paper.
pip install -r requirements.txt
To install Jax with cuda, run
pip install jax==0.3.0 -f "https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
pip install jaxlib==0.3.0 -f "https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
To train a train-time adversarial cheap talk channel against a CartPole PPO agent with two dimensions, run:
python3 main_train_time.py --rew_type="ANTI" --normalize-obs --end-only --env="CARTPOLE" --n_dims=2
To train a train/test-time exploiting adversarial cheap talk channel against a CartPole PPO agent, run:
python3 main_test_time.py --static --zero-shot --normalize-obs --env="CARTPOLE" --n_dims=2
@article{lu2022adversarial,
title={Adversarial Cheap Talk},
author={Lu, Chris and Willi, Timon and Letcher, Alistair and Foerster, Jakob},
journal={arXiv preprint arXiv:2211.11030},
year={2022}
}