  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.
