The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify. To address these problems we introduce TorchDriveSim and its benchmark extension TorchDriveEnv. TorchDriveEnv is a lightweight reinforcement learning benchmark programmed entirely in Python, which can be modified to test a number of different factors in learned vehicle behavior, including the effect of varying kinematic models, agent types, and traffic control patterns. Most importantly unlike many replay based simulation approaches, TorchDriveEnv is fully integrated with a state of the art behavioral simulation API. This allows users to train and evaluate driving models alongside data driven Non-Playable Characters (NPC) whose initializations and driving behavior are reactive, realistic, and diverse. We illustrate the efficiency and simplicity of TorchDriveEnv by evaluating common reinforcement learning baselines in both training and validation environments. Our experiments show that TorchDriveEnv is easy to use, but difficult to solve.

通过使用全面集成的行为仿真API，TorchDriveEnv提供了一个易于使用且易于修改的轻量级Python强化学习基准，用于训练和评估驾驶模型，并与数据驱动的非玩家角色（NPC）进行比较，其初始化和驾驶行为具有反应性、逼真性和多样性，难易程度相对较高。

TorchDriveEnv: 自主驾驶的强化学习基准测试 — 具有反应性、现实性和多样性的非可玩角色