Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization

arXiv:2604.14765 · cs.LG, math.OC, math.PR · Submitted 2026-04-16 · Read on arXiv

cs.LG, math.OC, math.PR

Submitted: 2026-04-16

Updated: 2026-09-16

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Action Space — 15× in this paper · explained in 1 episode
• Gradient Flow — 14× in this paper · explained in 2 episodes
• World Model — 13× in this paper · explained in 7 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Adam — 8× in this paper · explained in 2 episodes
• Optimal Transport — 8× in this paper · explained in 5 episodes
• Wasserstein Distance — 6× in this paper · explained in 4 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Objective Function — 4× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 4× in this paper · explained in 24 episodes
• Wasserstein Gradient Flow — 4× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• In International Conference on Machine Learning — 3× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adjoint Method — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Wasserstein Gradient Flow (WGF) — 1× in this paper · explained in 2 episodes

Sources

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