arXiv Machine Learning September 30, 2026 By Sergio Calo, Amy Zhang, Javier Segovia-Aguas, Anders Jonsson

Differentiating Bisimulation Metrics: A Framework for Parametric Markov Chain Fitting via Bicausal Optimal Transport

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robotics

arXiv AI

2d ago

Optimal Transport Meets Reinforcement Learning: A Survey

arXiv:2610.01413v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and e...

By Yujie Zhu, Charles A. Hepburn, Matthew Thorpe, Giovanni Montana

reinforcement-learning

arXiv AI

Jul 21

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning

arXiv:2607. 18004v1 Announce Type: new Abstract: Many visual reinforcement learning (RL) algorithms learn representations by matching latent distances to a behavioral distance induced by reward and transition similarity.

By Daegyeong Roh, Juho Bae, Han-Lim Choi

reinforcement-learning

arXiv Machine Learning

Jun 10

MODIP: Efficient Model-Based Optimization for Diffusion Policies

arXiv:2606. 10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC).

By Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome, Olivier Sigaud

diffusion reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jul 17

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607. 14522v1 Announce Type: new Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC).

By Zikun Zhang, Jiayuan Sheng, David D. Yao, Wenpin Tang

llms diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

2d ago

Fast Regularized Policy Mirror Descent with One-Step TD Updates

arXiv:2609.39837v1 Announce Type: new Abstract: Policy mirror descent (PMD) enjoys fast convergence in regularized Markov decision processes (MDPs), but existing guarantees often rely on exact or inc...

By Qipei Chen, Wenye Li, Yule Sun, Ke Wei

reinforcement-learning

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.

By Donggyu Min, Seongjin Choi, Dong-Kyu Kim

agents reinforcement-learning

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Related stories

Optimal Transport Meets Reinforcement Learning: A Survey

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning

MODIP: Efficient Model-Based Optimization for Diffusion Policies

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

Fast Regularized Policy Mirror Descent with One-Step TD Updates

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment