DriverGym: Democratising Reinforcement Learning for Autonomous Driving

2021/11/12 by Parth Kothari, Christian S. Perone, Kothari, Parth +7 · 2 citations
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2111.06889

openalex publication_date 2021/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data. We propose DriverGym, an open-source OpenAI Gym-compatible environment specifically tailored for developing RL algorithms for autonomous driving. DriverGym provides access to more than 1000 hours of expert logged data and also supports reactive and data-driven agent behavior. The performance of an RL policy can be easily validated on real-world data using our extensive and flexible closed-loop evaluation protocol. In this work, we also provide behavior cloning baselines using supervised learning and RL, trained in DriverGym. We make DriverGym code, as well as all the baselines publicly available to further stimulate development from the community.

Citations

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• A Reduction of Imitation Learning and Structured Prediction to No-Regret\n Online Learning
• ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
• SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

Cited by

• UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos
• TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning

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