arXiv Machine Learning

Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning

arXiv Machine Learning

Jul 15

Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning

arXiv:2607. 12590v1 Announce Type: cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment.

By Amber Srivastava

agents reinforcement-learning

arXiv Machine Learning

Jun 2

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability

arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.

By Volodymyr Tkachuk, Csaba Szepesv\'ari, Xiaoqi Tan

reinforcement-learning

arXiv Machine Learning

Jun 16

Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks

arXiv:2606. 16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks.

By Eslam Eldeeb, Hirley Alves

diffusion reinforcement-learning efficiency safety

arXiv AI

Jun 19

A Multi-Agent system for Multi-Objective constrained optimization

arXiv:2606. 20236v1 Announce Type: new Abstract: Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints.

By Federica Filippini

rag agents reinforcement-learning

arXiv Machine Learning

Aug 24

Reinforcement Learning for Continuous-Time Jump Markov Decision Processes with Applications to Network Dynamic Pricing

The paper introduces reinforcement learning for Continuous-Time Jump Markov Decision Processes (CTJMDPs) with general discrete state spaces and continuous/discrete actions. It develops entropy‑regularized continuous‑time control and establishes theoretical foundations for q‑learning in this setting, providing model‑free algorithms that outperform naive discretization. Numerical tests on network dynamic pricing demonstrate the method’s ability to learn near‑optimal policies and scale to large networks.

By Huiling Meng, Ningyuan Chen, Xuefeng Gao

reinforcement-learning benchmarks

arXiv AI

Aug 6

Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks

arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.

By Xiao Wang, Shun-Ren Yang

reinforcement-learning

arXiv Machine Learning

Jun 10

Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning

arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.

By Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang

reinforcement-learning safety

arXiv AI

Jun 10

Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets

arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.

By Yi Chen (Lucy), Rushuai Yang (Lucy), Qiang Chen (Lucy), Dongyan (Lucy), Huo

reinforcement-learning benchmarks

arXiv AI

Aug 26

Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectives

arXiv:2304.10041v2 Announce Type: replace Abstract: This work investigates formal policy synthesis for continuous-state stochastic dynamic systems subject to high-level specifications expressed in li...

By Lening Li, Zhentian Qian, Jianan Xia, Qiren Geng, Huasheng Zhang, Liang Hu, Qishuang Li, Junqiang Lou

reinforcement-learning efficiency

arXiv AI

Jun 18

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

arXiv:2606. 18308v1 Announce Type: cross Abstract: Safe coordination in networked cyber-physical systems forces learning algorithms to simultaneously handle hybrid discrete-continuous actions, hard training-time safety constraints, and physics-governed dynamics.

By Zijie Meng, Ziwei Li, Yufei Liu, Zhiyu Li, Jiyuan Liu, Wenhua Nie, Bingcai Wei, Miao Zhang

agents reinforcement-learning safety

arXiv AI

Jul 21

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.

By Yue Zhong, Jiawen Kang, Yongju Tong, Hong-Ning Dai, Dong In Kim, Abbas Jamalipour, Shengli Xie

reinforcement-learning efficiency

arXiv Machine Learning

Jul 22

Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces

arXiv:2607. 18554v1 Announce Type: cross Abstract: We develop the Continuous Distributed Coupled Policy Gradient (CDCPG) algorithm for cooperative reinforcement learning in networked Markov decision processes with continuous state and action spaces.

By Dongming Wang, Pengcheng Dai, Wenwu Yu, Wei Ren

agents reinforcement-learning benchmarks

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