论文标题
Nocturne：可扩展的驾驶基准测试，用于使多项式学习更接近现实世界
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
论文作者
论文摘要
我们介绍了Nocturne，这是一种新的2D驾驶模拟器，用于研究部分可观察性下的多代理协调。夜曲的重点是在现实世界多代理设置中对推理和心理理论进行研究，而没有计算机视觉的计算开销并从图像中提取特征。该模拟器中的代理只会观察到场景的障碍，模仿人类的视觉传感限制。与现有的基准通过直接使用摄像机输入渲染类似人类的观测来瓶颈的现有基准不同，夜曲使用有效的相交方法来计算C ++后端中的矢量化可见特征集，从而使模拟器可以在2000年以上的超过2000个步骤中运行。使用开源轨迹和映射数据，我们构建了一个模拟器，以加载和重播任意轨迹和现实世界驾驶数据的场景。使用这种环境，我们基准了加强学习和模仿学习剂，并证明这些代理远离人类水平的协调能力，并显着偏离了专家轨迹。
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.
