缩放即一切：基于JAX加速强化学习的自动驾驶
Scaling Is All You Need: Autonomous Driving with JAX-Accelerated Reinforcement Learning
AI总结：
本文利用JAX加速的强化学习系统，结合高效逼真的驾驶模拟器，在自动驾驶领域进行大规模实验，证明策略性能随规模提升，最佳策略降低失败率64%并提高驾驶进展率25%。
摘要：
强化学习已被证明在视频游戏等复杂领域能够超越最优秀的人类表现。然而，在自动驾驶所需的规模上运行强化学习实验极其困难。构建大规模强化学习系统并将其分布在众多GPU上具有挑战性。在真实世界车辆上训练期间收集经验在安全性和可扩展性方面是不可行的。因此，需要一个高效且逼真的驾驶模拟器，利用大量真实驾驶数据。我们将这些能力结合起来，为自动驾驶开展大规模强化学习实验。我们证明，随着规模增加，我们的策略性能得到提升。与最先进的自动驾驶机器学习策略相比，我们表现最佳的策略将失败率降低了64%，同时将驾驶进展率提高了25%。
英文摘要：
Reinforcement learning has been demonstrated to outperform even the best humans in complex domains like video games. However, running reinforcement learning experiments on the required scale for autonomous driving is extremely difficult. Building a large scale reinforcement learning system and distributing it across many GPUs is challenging. Gathering experience during training on real world vehicles is prohibitive from a safety and scalability perspective. Therefore, an efficient and realistic driving simulator is required that uses a large amount of data from real-world driving. We bring these capabilities together and conduct large-scale reinforcement learning experiments for autonomous driving. We demonstrate that our policy performance improves with increasing scale. Our best performing policy reduces the failure rate by 64% while improving the rate of driving progress by 25% compared to the policies produced by state-of-the-art machine learning for autonomous driving.
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。
