T3AS/MAD-ARL
Python project for the paper "Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies".
This project helps autonomous driving engineers and researchers rigorously test the safety and robustness of multi-agent self-driving car policies. It simulates complex driving scenarios within the CARLA environment, allowing you to feed in your autonomous driving algorithms and assess how well they perform against adversarial conditions and other vehicles. The output provides insights into the vulnerabilities and strengths of these policies, which is critical for developing safer autonomous systems.
No commits in the last 6 months.
Use this if you need to evaluate and improve the resilience of multi-agent autonomous driving systems against diverse and challenging scenarios.
Not ideal if you are looking for a general-purpose driving simulator for entertainment or basic policy development, as its focus is on adversarial testing.
Stars
14
Forks
3
Language
Python
License
GPL-3.0
Category
Last pushed
Feb 24, 2023
Commits (30d)
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