Hugging Face Trending Papers June 3, 2026

Policy Gradient for Continuous-Time Robust Markov Decision Processes

Read the original on Hugging Face Trending Papers →

The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics. Traditional RMDPs consider discrete-time dynamics and recently, sample-efficient policy gradient algorithms have been considered in this context.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jun 4

Policy Gradient for Continuous-Time Robust Markov Decision Processes

arXiv:2606. 04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics.

By Tanya Veeravalli, David M. Bossens, Atsushi Nitanda

agents reinforcement-learning safety

arXiv Machine Learning

Jun 16

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

arXiv:2606. 16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model.

By Jongmin Lee, Ernest K. Ryu, Vaneet Aggarwal

reinforcement-learning

arXiv Machine Learning

Jun 15

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

arXiv:2606. 14095v1 Announce Type: new Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model.

By Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie

reinforcement-learning safety

arXiv Machine Learning

Sep 22

Reinforcement learning with an expectile-based objective

arXiv:2602. 09300v2 Announce Type: replace Abstract: We consider the policy evaluation and control in a finite horizon reinforcement learning (RL) setting under an expectile-based objective.

By Shrey Rakeshkumar Patel, Sumedh Gupte, Soumen Pachal, Prashanth L. A., Sanjay P. Bhat

reinforcement-learning benchmarks

arXiv Machine Learning

Sep 15

High-Probability Nash Regret for Decentralized Learning in Markov $\alpha$-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games

arXiv:2609. 14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov $\alpha$-potential games.

By S. Rasoul Etesami

reinforcement-learning safety

arXiv Machine Learning

Jul 28

Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes

arXiv:2607. 22982v1 Announce Type: new Abstract: Natural Policy Gradient (NPG) is a well-established Reinforcement Learning algorithm that underlies widely used methods such as Trust Region Policy Optimization and Proximal Policy Optimization, both of which have demonstrated strong empirical success.

By Asha Barua, Sajad Khodadadian

reinforcement-learning

One email a morning, machine-written

Related stories

Policy Gradient for Continuous-Time Robust Markov Decision Processes

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

Reinforcement learning with an expectile-based objective

High-Probability Nash Regret for Decentralized Learning in Markov $\alpha$-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games

Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes