Optimal Transport Meets Reinforcement Learning: A Survey
stat.ML, cs.AI, cs.LG
Submitted: 2026-10-01
Updated: 2026-10-01
Code: https://github.com/PythonOT/POT
Terminology
Sources
- Wasserstein Robust Reinforcement Learning
- Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
- Risk-Aware Reinforcement Learning through Optimal Transport Theory
- Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning
- The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning
- The Cramer Distance as a Solution to Biased Wasserstein Gradients
- Align Your Intents: Offline Imitation Learning via Optimal Transport
- Wasserstein Distance Maximizing Intrinsic Control
- Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
- Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
- Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem
- Robust Reinforcement Learning with Wasserstein Constraint
- A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
- Safe Wasserstein Constrained Deep Q-Learning
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
- Improved Complexity Analysis of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport
- Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport
- Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters
- On Wasserstein Reinforcement Learning and the Fokker-Planck equation
- Online Distributionally Robust LLM Alignment via Regression to Relative Reward
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