  Offline reinforcement learning (RL) Algorithms are often designed with
environments such as MuJoCo in mind, in which the planning horizon is extremely
long and no noise exists. We compare model-free, model-based, as well as hybrid
offline RL approaches on various industrial benchmark (IB) datasets to test the
algorithms in settings closer to real world problems, including complex noise
and partially observable states. We find that on the IB, hybrid approaches face
severe difficulties and that simpler algorithms, such as rollout based
algorithms or model-free algorithms with simpler regularizers perform best on
the datasets.
