Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving
This work addresses the problem of improving safety in autonomous driving by efficiently finding failure cases for rule-based algorithms, though it is incremental as it builds on existing adversarial reinforcement learning techniques.
The paper tackles the challenge of identifying failure scenarios for rule-based agents in safety-critical multi-agent domains like autonomous driving, proposing a method that trains adversarial agents via multi-agent reinforcement learning to induce failures, and demonstrates its effectiveness with a simple environment and autonomous driving simulator, showing concrete improvements in failure detection efficiency.
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.