Here, we focus ourattention onthesecond case, which leads toscore improvement without anyre-training.

We propose RLlib Flow, a hybrid actor-dataflow programming model for distributed RL, and validate its practicality by porting the full suite of algorithms in RLlib, a widely adopted distributed RL library.

In offline RL, a critical challenge is distribution shift (also called "extrapolation error" in literature).

Generalization to new environments remains a major challenge in deep reinforcement learning (RL). Current methods fail to generalize to unseen environments even when trained on similar settings [19, 51, 71, 11, 21, 12, 60].