Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving

Understand

Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks.

• However, unlike supervised machine learning, learning strategies that generalize well to a wide range of situations remains one of the most challenging problems for real-world RL.
• Autonomous driving (AD) provides a multi-faceted experimental field, as it is necessary to learn the correct behavior over many variations of road layouts and large distributions of possible traffic situations, including individual driver personalities and hard-to-predict traffic events.
• In this paper we propose a challenging benchmark for generalizable RL for AD based on a configurable, flexible, and performant code base.