initial commit

.gitignore

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+# Compiled sources
+*.pyc
+build/
+dist/
+
+# Setup
+**.egg*
+
+# Jupyter notebooks
+**.ipynb_checkpoints*
+
+# Sphinx documentation
+_build
+
+# Editor files
+.idea
+.vscode
+
+# Test files
+.pytest_cache
+.cache
+
+# Outputs
+**/out/*
+runs/
+saved_models/
+
+# Docs
+docs/_build
+**jupyter_execute

README.md

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+# Aggresiveness-regulated Multi-agent Stress Testing of Autonomous Vehicles
+
+This repo dedicates to the task of stress testing a black-box autnomous vehicle system using multi-agent reinforcement learnign algorithms under an adversarial environment.
+
+The simulation environment used are included as a submodule, which is adapted from [highway-env](https://github.com/Farama-Foundation/HighwayEnv).
+
+The codes in this repo follow the suggested one-file implementation in reinforcement learning community.
+
+There are four algorithms experimented in this repo:
+- [MACPO](https://arxiv.org/abs/2110.02793) (Multi-Agent Constrained Policy Optimization)
+- [HATRPO](https://arxiv.org/abs/2109.11251) (Heterogeneous-Agent Trust Region Policy Optimization)
+- [CPO](https://arxiv.org/abs/1705.10528) (Constrained Policy Optimization)
+- [TRPO](https://arxiv.org/abs/1502.05477) (Trust Region Policy Optimization)
+
+The results are summarized in this [thesis](http://hdl.handle.net/10012/19897)
+
+## Environment Setup
+The python environemnt can be created using conda:
+```bash
+conda env create -f environment.yml
+```
+
+## Running
+To run the training script of an algorithm `ALGO` listed above `ALGO.py`:
+```bash
+python ALGO.py
+```
+To run the testing script of an algorithm `ALGO` listed above `ALGO_vis.py`:
+```bash
+python ALGO_vis.py
+```
+
+## Acknowledgments
+The code within this repository was developed with assistance from [MACPO](https://github.com/chauncygu/Multi-Agent-Constrained-Policy-Optimisation) and [CleanRL](https://github.com/vwxyzjn/cleanrl/tree/master/cleanrl).