D4Rl

D4RL (Diverse Off-Road Robotics Learning) is an open-source benchmark library designed for offline reinforcement learning research, aiming to provide a variety of robotic learning tasks and datasets. By standardizing task settings and evaluation metrics, D4RL promotes algorithm comparability and reproducibility, accelerating the development and application of offline reinforcement learning technologies. The library covers a range of tasks from simple to complex, supporting researchers in exploring learning strategies in different environments and enhancing the autonomous decision-making capabilities of robots.