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Simulation is an essential tool to develop and benchmark autonomous vehicleplanning software in a safe and cost-effective manner. However, realisticsimulation requires accurate modeling of nuanced and complex multi-agentinteractive behaviors. To address these challenges, we introduce Waymax, a newdata-driven simulator for autonomous driving in multi-agent scenes, designedfor large-scale simulation and testing. Waymax uses publicly-released,real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize orplay back a diverse set of multi-agent simulated scenarios. It runs entirely onhardware accelerators such as TPUs/GPUs and supports in-graph simulation fortraining, making it suitable for modern large-scale, distributed machinelearning workflows. To support online training and evaluation, Waymax includesseveral learned and hard-coded behavior models that allow for realisticinteraction within simulation. To supplement Waymax, we benchmark a suite ofpopular imitation and reinforcement learning algorithms with ablation studieson different design decisions, where we highlight the effectiveness of routesas guidance for planning agents and the ability of RL to overfit againstsimulated agents.

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research