Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment. This may cause the learned driving policy to overfit the environment, making it difficult to interact well with vehicles with different, unseen behaviors. In this work, we introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy. By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy through guiding policies that achieve specific objectives. We further propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy. Our method successfully learns an ego driving policy that generalizes well to unseen situations with out-of-distribution (OOD) social agents' behaviors in a challenging uncontrolled T-intersection scenario.

通过随机化基于交互的社交车辆的奖励函数，本研究引入了一种高效的方法来训练多样化的社交车辆驾驶策略作为单一的元策略，并提出了一种训练策略来增强自车驾驶策略的鲁棒性。该方法成功地学习到了在具有挑战性的未控制T字形交叉口情景中，对具有分布之外行为的社交车辆场景具有很好泛化性的自车驾驶策略。

引导元强化学习下的鲁棒驾驶策略学习