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

Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving