D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Source
- Raw Markdown: paper_d4rl-2020.md
- PDF: paper_d4rl-2020.pdf
Core Claim
D4RL packages offline RL trajectories as state-action-reward-next-state datasets across locomotion, navigation, dexterous manipulation, and kitchen tasks.
Action-Time-Series Notes
- Treats time as episodic transition sequences rather than regularly sampled calendar time.
- Action channel is explicit and is usually the environment control vector.
- Useful as a clean low-dimensional starting point for action-conditioned dynamics and model-based offline RL.