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