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Python project for the paper "Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies".

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License: GPL v3

MAD-ARL is a multi-agent adversarial testing platform built on top of the CARLA Autonomous Driving simulator and Macad-Gym.

Installattion Setup

  1. Install the system requirements:

    • Ubuntu 18.04+
    • Anaconda (latest version)
    • cmake (sudo apt install cmake)
    • zlib (sudo apt install zlib1g-dev)
    • [optional] ffmpeg (sudo apt install ffmpeg)
  2. Setup CARLA (0.9.4):

Run mkdir ~/software && cd ~/software

Download the 0.9.4 release version from: Here Extract it into ~/software/CARLA_0.9.4

Run echo "export CARLA_SERVER=${HOME}/software/CARLA_0.9.4/CarlaUE4.sh" >> ~/.bashrc

  1. Install MAD-ARL:

Fork/Clone the repository to your workspace: git clone https://github.com/AizazSharif/MAD-ARL.git && cd MAD-ARL

Create a new conda env named "macad-gym" and install the required packages: conda env create -f conda_env.yml

Activate the environment: conda activate MAD-ARL

Run the following commands in sequence for installing rest of the packages to avoid version errors:

pip install -e .

pip install --upgrade pip

pip install -e .

pip install tensorflow==2.1.0

pip install tensorflow-gpu==2.1.0

pip install pip install ray[tune]==0.8.4

pip install pip install ray[rllib]==0.8.4

pip install tf_slim

pip install tensorboardX==2.1

Instructions

Soon to be updated here along with the paper.

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Python project for the paper "Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies".

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