D4RL

The D-4RL dataset/benchmark contains various reinforcement learning tasks designed to evaluate the performance of algorithms in offline reinforcement learning settings, specifically in environments like maze2d and antmaze.

Papers using D4RL (5)

Neuro-Inspired Inverse Learning for Planning and Control2026Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations2026Off-Policy Actor-Critic with Sigmoid-Bounded Entropy for Real-World Robot Learning2026Opinion: Towards Unified Expressive Policy Optimization for Robust Robot Learning2025Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising2025