Optimal Transport Meets Reinforcement Learning: A Survey
Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and experts, action distributions from learned policies and offline datasets, or transition distributions from learned models and environments. However, commonly used divergences may become ineffective when these distributions overlap weakly, which is frequently encountered in imitation learning, offline RL, and deployment under distribution shift. Optimal transport (OT) offers an alternative by measuring the cost of \emph{moving} probability mass from one distribution to another under a ground cost that encodes task geometry. This survey covers h...
| Authors | Yujie Zhu, Charles A. Hepburn, Matthew Thorpe et al. |
|---|---|
| Primary category | stat.ML |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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