  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.
