中文标题#
基于深度强化学习的自动驾驶中对抗性智能体行为学习
英文标题#
Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning
中文摘要#
现有的强化学习方法训练一个智能体在具有基于规则的周围智能体的环境中学习期望的最优行为。 在安全关键的应用中，如自动驾驶，正确建模基于规则的智能体至关重要。 目前使用了多种行为建模策略和 IDM 模型来对周围智能体进行建模。 我们提出了一种基于学习的方法，以推导出导致失败场景的对抗性行为。 我们对所有基于规则的智能体评估了我们的对抗智能体，并展示了累积奖励的减少。
英文摘要#
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.
文章页面#
PDF 获取#
抖音扫码查看更多精彩内容