arXiv AI

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.

arXiv AI

Jun 2

Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

arXiv:2606. 02337v1 Announce Type: new Abstract: Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requirements couple agents in ways that reward structure alone does not capture.

By Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson

agents reinforcement-learning

arXiv AI

Aug 3

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

arXiv:2607. 29559v1 Announce Type: new Abstract: Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function.

By Manith Adikari, Bei Peng, Samuele Vinanzi, Angelo Cangelosi

agents reinforcement-learning benchmarks

arXiv AI

Jul 22

Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning

arXiv:2607. 19117v1 Announce Type: new Abstract: Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization.

By Ubayd Ali Bapoo, Clement N Nyirenda

agents reinforcement-learning benchmarks

arXiv AI

Jun 19

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.

By Ngoc Hung Nguyen, Nguyen Van Thieu, Quang-Trung Luu, Anh Tuan Nguyen, Senura Wanasekara, Nguyen Cong Luong, Fatemeh Kavehmadavani, Van-Dinh Nguyen

agents reinforcement-learning

arXiv AI

Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

llms agents reinforcement-learning fine-tuning

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 AI

Aug 7

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

arXiv:2608. 05346v1 Announce Type: cross Abstract: Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR).

By Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo

agents reinforcement-learning

arXiv Machine Learning

Jun 25

Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL

arXiv:2606. 25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constraints.

By Ankur Naskar, Swetha Ganesh, Vaneet Aggarwal

agents reinforcement-learning safety

arXiv Machine Learning

Aug 11

Multi-Agent Reinforcement Learning via Agent-Specific Preference

arXiv:2608. 08604v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions.

By Ni Mu, Yao Luan, Yiqin Yang, Qing-Shan Jia

agents reinforcement-learning benchmarks

arXiv AI

Jul 29

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.

By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm

benchmarks

arXiv Machine Learning

Jul 1

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.

By Tommaso Marzi, Cesare Alippi, Andrea Cini

agents reinforcement-learning benchmarks

arXiv AI

Jun 24

Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems

arXiv:2606. 24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.

By Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, Tony Q. S. Quek

rag agents

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