TorchDriveEnv：一个面向自动驾驶、具有反应性、真实性和多样性非玩家角色的强化学习基准
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
AI总结：
本文提出 Python 强化学习自动驾驶基准 TorchDriveEnv，集成行为仿真 API 与反应、真实且多样的数据驱动 NPC，并通过 RL 基线验证其易用而难解。
摘要：
自动驾驶车辆的训练、测试和部署需要真实且高效的模拟器。此外，由于不同自动驾驶系统所呈现的不同问题之间存在高度差异性，这些模拟器需要易于使用且易于修改。为解决这些问题，我们提出了 TorchDriveSim 及其基准扩展 TorchDriveEnv。TorchDriveEnv 是一个完全使用 Python 编写的轻量级强化学习基准，可被修改以测试学习到的车辆行为中的多种不同因素，包括变化的运动学模型、智能体类型和交通控制模式的影响。最重要的是，与许多基于回放的仿真方法不同，TorchDriveEnv 与最先进的行为仿真 API 完全集成。这使用户能够在数据驱动的非玩家角色（NPC）旁训练和评估驾驶模型，这些 NPC 的初始化和驾驶行为具有反应性、真实性和多样性。我们通过在训练和验证环境中评估常见的强化学习基线，展示了 TorchDriveEnv 的效率和简洁性。实验表明，TorchDriveEnv 易于使用，但难以求解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
