D4RL

D-4RL is a benchmark that contains a diverse set of offline reinforcement learning tasks used to evaluate the performance of algorithms in generating policies and learning from previously collected data.

Papers using D4RL (2)

Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations2026Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training2024