D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets that allow researchers to train and evaluate algorithms without requiring online interaction with the environment. The project includes robotics environments and has been succeeded by newer libraries (Minari for datasets, Gymnasium-Robotics for environments), but the core library remains the canonical reference for offline RL benchmarking.