# Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2305.18380 · HTML: https://mlsanity.com/paper/2305.18380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 28 May 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning](https://mlsanity.com/paper/2206.15378/) (2022)
- [Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL](https://mlsanity.com/paper/2109.07960/) (2021)
- [ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2109.02529/) (2021)
- [Addressing the IEEE AV Test Challenge with Scenic and VerifAI](https://mlsanity.com/paper/2108.13796/) (2021)
- [Shaping Advice in Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2103.15941/) (2021)
- [ComOpT: Combination and Optimization for Testing Autonomous Driving Systems](https://mlsanity.com/paper/2110.00761/) (2021)
- [An Empirical Testing of Autonomous Vehicle Simulator System for Urban Driving](https://mlsanity.com/paper/2108.07910/) (2021)

## Abstract (arXiv)

While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-making module. One of the critical challenges for collaborative RL is the credit assignment problem, where a proper assignment of rewards to multiple agents interacting in the traffic scenario, considering all parameters and timing, turns out to be non-trivial. In order to address this challenge, we propose a novel potential-based reward-shaping approach inspired by counterfactual analysis for solving the credit-assignment problem. The evaluation in a simulated environment demonstrates the superiority of our proposed approach against other methods using local and global rewards.