GPUDrive：以每秒100萬幀的速度進行數據驅動的多智能駕駛模擬。

摘要

多智能體學習算法在各種遊戲中成功生成超越人類水準的規劃，但對部署的多智能體規劃器設計影響有限。應用這些技術於多智能體規劃的一個關鍵瓶頸是它們需要數十億步的經驗。為了在這個規模上進行多智能體規劃的研究，我們提出了GPUDrive，這是一個基於Madrona遊戲引擎構建的GPU加速多智能體模擬器，每秒可以生成超過一百萬步的經驗。觀察、獎勵和動力學函數直接用C++編寫，使用戶可以定義複雜、異構的智能體行為，並轉換為高性能CUDA。我們展示了使用GPUDrive，我們能夠在Waymo運動數據集的許多場景中有效訓練強化學習智能體，為個別場景在幾分鐘內產生高效的目標達成智能體，一般具有能力的智能體則需要幾小時。我們將這些訓練過的智能體作為代碼庫的一部分發布在https://github.com/Emerge-Lab/gpudrive。

English

Multi-agent learning algorithms have been successful at generating superhuman planning in a wide variety of games but have had little impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at this scale, we present GPUDrive, a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine that can generate over a million steps of experience per second. Observation, reward, and dynamics functions are written directly in C++, allowing users to define complex, heterogeneous agent behaviors that are lowered to high-performance CUDA. We show that using GPUDrive we are able to effectively train reinforcement learning agents over many scenes in the Waymo Motion dataset, yielding highly effective goal-reaching agents in minutes for individual scenes and generally capable agents in a few hours. We ship these trained agents as part of the code base at https://github.com/Emerge-Lab/gpudrive.

GPUDrive：以每秒100萬幀的速度進行數據驅動的多智能駕駛模擬。

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS

摘要

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