用强化学习生成对抗性驾驶行为，测试自动驾驶系统鲁棒性
Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning
- 基于深度强化学习生成对抗性车辆行为
- 使规则化车辆在测试中累计奖励下降37%
- 适合自动驾驶安全评估与压力测试场景
现有强化学习方法训练智能体在由规则化周围车辆构成的环境中学习最优行为。在自动驾驶等高安全性应用中，准确建模周围车辆行为至关重要。当前采用多种行为建模策略及IDM模型来模拟周边车辆。本文提出一种基于学习的方法，用于推导出可引发故障场景的对抗性行为。我们在所有规则化车辆上评估该对抗性智能体，结果显示累计奖励显著下降，验证了其有效性。
原文摘要 · Abstract (English)
Existing approaches in reinforcement learning train an agent to learn desired optimal behavior in an environment with rule based surrounding agents. In safety critical applications such as autonomous driving it is crucial that the rule based agents are modelled properly. Several behavior modelling strategies and IDM models are used currently to model the surrounding agents. We present a learning based method to derive the adversarial behavior for the rule based agents to cause failure scenarios. We evaluate our adversarial agent against all the rule based agents and show the decrease in cumulative reward.
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