We introduce Nocturne, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference and theory of mind in real-world multi-agent settings without the computational overhead of computer vision and feature extraction from images. Agents in this simulator only observe an obstructed view of the scene, mimicking human visual sensing constraints. Unlike existing benchmarks that are bottlenecked by rendering human-like observations directly using a camera input, Nocturne uses efficient intersection methods to compute a vectorized set of visible features in a C++ back-end, allowing the simulator to run at over 2000 steps-per-second. Using open-source trajectory and map data, we construct a simulator to load and replay arbitrary trajectories and scenes from real-world driving data. Using this environment, we benchmark reinforcement-learning and imitation-learning agents and demonstrate that the agents are quite far from human-level coordination ability and deviate significantly from the expert trajectories.

翻译：我们提出Nocturne，一种新型二维驾驶模拟器，用于研究部分可观测条件下的多智能体协作。Nocturne的核心目标是在避免计算机视觉及图像特征提取计算开销的前提下，推动真实世界多智能体场景中的推理与心智理论研究。该模拟器中的智能体仅能观测场景的局部受限视野，模拟人类视觉感知的局限性。与现有基准通过摄像头输入直接渲染类人观测的瓶颈不同，Nocturne采用高效的相交计算方法，在C++后端计算一组矢量化的可见特征，使模拟器能以每秒超过2000步的速度运行。基于开源轨迹与地图数据，我们构建了一个可加载并回放真实驾驶数据中任意轨迹与场景的模拟器。利用该环境，我们对强化学习与模仿学习智能体进行基准测试，结果表明这些智能体的协作能力远未达到人类水平，且与专家轨迹存在显著偏差。