通过社会对抗性交通流实现驾驶策略的零样本迁移学习
Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow
AI总结：
针对密集交通流中驾驶策略难以零样本迁移的问题，提出以SVO为情境的社会对抗性交通流及两阶段分层对抗训练框架，显著提升策略在未见场景中的迁移性能。
摘要：
在密集交通流中驾驶时，获得能够迁移到未见环境的驾驶策略颇具挑战性。交通流的设计至关重要，而以往研究无法在交互性与安全关键性之间取得平衡。为解决这一问题，我们提出了一种社会对抗性交通流。我们提出一种情境部分可观测随机博弈来对交通流建模，并将社会价值取向（SVO）作为情境。随后，我们采用两阶段框架。在阶段1中，我们具备社会意识的交通流中的每个智能体都由一个分层策略驱动，其中上层策略传达所有智能体的真实SVO，下层策略将其作为输入。在阶段2中，社会对抗性交通流中的每个智能体都由分层策略驱动，其中上层传达错误的SVO，并由阶段1中训练的下层策略接收。驾驶策略通过与上层策略的零和博弈形式进行对抗训练，从而得到一个向未见交通流的零样本迁移能力增强的策略。关于交叉验证的综合实验验证了我们方法优越的零样本迁移性能。
英文摘要：
Acquiring driving policies that can transfer to unseen environments is challenging when driving in dense traffic flows. The design of traffic flow is essential and previous studies are unable to balance interaction and safety-criticism. To tackle this problem, we propose a socially adversarial traffic flow. We propose a Contextual Partially-Observable Stochastic Game to model traffic flow and assign Social Value Orientation (SVO) as context. We then adopt a two-stage framework. In Stage 1, each agent in our socially-aware traffic flow is driven by a hierarchical policy where upper-level policy communicates genuine SVOs of all agents, which the lower-level policy takes as input. In Stage 2, each agent in the socially adversarial traffic flow is driven by the hierarchical policy where upper-level communicates mistaken SVOs, taken by the lower-level policy trained in Stage 1. Driving policy is adversarially trained through a zero-sum game formulation with upper-level policies, resulting in a policy with enhanced zero-shot transfer capability to unseen traffic flows. Comprehensive experiments on cross-validation verify the superior zero-shot transfer performance of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
