AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning
  with Value-based Dataset
Dongsu Lee Chanin Eom Minhae Kwon
Offline reinforcement learning has emerged as a promising technology by
enhancing its practicality through the use of pre-collected large datasets.
Despite its practical benefits, most algorithm development research in offline
reinforcement learning still relies on game tasks with synthetic datasets. To
address such limitations, this paper provides autonomous driving datasets and
benchmarks for offline reinforcement learning research. We provide 19 datasets,
including real-world human driver's datasets, and seven popular offline
reinforcement learning algorithms in three realistic driving scenarios. We also
provide a unified decision-making process model that can operate effectively
across different scenarios, serving as a reference framework in algorithm
design. Our research lays the groundwork for further collaborations in the
community to explore practical aspects of existing reinforcement learning
methods. Dataset and codes can be found in https://sites.google.com/view/ad4rl.