Offline RL benchmark datasets in highly stochastic domains introduced in the paper "One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning", published at NeurIPS 2023.
For the accompanying code, please see the Github repo at github.com/marc-rigter/1R2R.
