中文标题#
基於深度強化學習的自動駕駛中對抗性智能體行為學習
英文标题#
Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning
中文摘要#
現有的強化學習方法訓練一個智能體在具有基於規則的周圍智能體的環境中學習期望的最優行為。 在安全關鍵的應用中，如自動駕駛，正確建模基於規則的智能體至關重要。 目前使用了多種行為建模策略和 IDM 模型來對周圍智能體進行建模。 我們提出了一種基於學習的方法，以推導出導致失敗場景的對抗性行為。 我們對所有基於規則的智能體評估了我們的對抗智能體，並展示了累積獎勵的減少。
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.
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