  We examine the problem of adversarial reinforcement learning for multi-agent
domains including a rule-based agent. Rule-based algorithms are required in
safety-critical applications for them to work properly in a wide range of
situations. Hence, every effort is made to find failure scenarios during the
development phase. However, as the software becomes complicated, finding
failure cases becomes difficult. Especially in multi-agent domains, such as
autonomous driving environments, it is much harder to find useful failure
scenarios that help us improve the algorithm. We propose a method for
efficiently finding failure scenarios; this method trains the adversarial
agents using multi-agent reinforcement learning such that the tested rule-based
agent fails. We demonstrate the effectiveness of our proposed method using a
simple environment and autonomous driving simulator.
