CoRL 2020 Best System Paper Winner: Noah’s Ark Lab Multi-Agent RL Simulation for Autonomous Driving

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The CoRL 2020 Best System Paper Award was presented today to Huawei Noah’s Ark Lab, Shanghai Jiao Tong University and University College London for their paper SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. The CoRL 2020 Award Committee praised the work as “a thorough and well-thought-out system with strong potential impact for the Autonomous Driving community.“The paper introduces SMARTS (Scalable Multi-Agent RL Training School), a realistic multi-agent simulation platform for autonomous driving. SMARTS supports the training, accumulation, and use of diverse road user behaviour models to help reinforcement learning (RL) researchers examine realistic road interaction scenarios. It has been open-sourced.While exploring the open road can be exhilarating, the driving experience more typically involves navigating busy streets packed with other, often unpredictable drivers. For autonomous driving vehicles, interactions with the wide range of intelligent and not-so-intelligent other road users present a fundamental challenge with very high stakes.The researchers identify a pain point: “Current mainstream level-4 AD (Autonomous Driving) solutions tend to limit interaction rather than embrace it: when encountering complexly interactive scenarios, the autonomous car tends to slow down and wait rather than acting proactively to find another way through.”The team notes that in California …

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