Tootfinder

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2506.04626

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing m…

Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
Maxime Toquebiau, Jae-Yun Jun, Fa\"iz Benamar, Nicolas Bredeche
https://arxiv.org/abs/2506.05236

Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
Communication is a fundamental aspect of coordinated behavior in multi-agent reinforcement learning. Yet, most prior works in this field have focused on emergent communication protocols developed from scratch, often resulting in inefficient or non-interpretable systems. Inspired by the role of language in natural intelligence, we investigate how grounding agents in a human-defined language can improve learning and coordination of multiple embodied agents. We propose a framework in which agents …

Robust and Safe Multi-Agent Reinforcement Learning Framework with Communication for Autonomous Vehicles
Keshawn Smith, Zhili Zhang, H M Sabbir Ahmad, Ehsan Sabouni, Maniak Mondal, Song Han, Wenchao Li, Fei Miao
https://arxiv.org/abs/2506.00982

Robust and Safe Multi-Agent Reinforcement Learning Framework with Communication for Autonomous Vehicles
Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for many multi-robot problems. For autonomous vehicles, the development of vehicle-to-vehicle (V2V) communication technologies provide opportunities to further enhance safety of the system. However, zero-shot transfer of simulator-trained MARL policies to hardware dynamic systems remains challenging, and how to leverage communication and shared information for MARL has limited demonstrations on hardw…

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Ahmet Melih Ince, Ayse Elif Canbilen, Halim Yanikomeroglu
https://arxiv.org/abs/2508.00011 h…

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication rel…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Molly Wang
https://arxiv.org/abs/2507.22174 https://arxiv.org/pdf/25…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Reinforcement Learning (RL) has become a well-established approach for optimizing packet routing in communication networks. Standard RL algorithms typically are based on the Markov Decision Process (MDP), which assumes that the current state of the environment provides all the necessary information for system evolution and decision-making. However, this Markovian assumption is invalid in many practical scenarios, making the MDP and RL frameworks inadequate to produce the optimal solutions. Addi…

Cryptanalysis of LC-MUME: A Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices
Ramprasad Sarkar
https://arxiv.org/abs/2507.22674 https://

Cryptanalysis of LC-MUME: A Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices
Yang et al. proposed a lightweight certificateless multiuser matchmaking encryption (LC-MUME) scheme for mobile devices, published in IEEE Transactions on Information Forensics and Security (TIFS) (DOI: 10.1109/TIFS.2023.3321961). Their construction aims to reduce computational and communication overhead within a one-to-many certificateless cryptographic framework. The authors claim that their scheme satisfies existential unforgeability under chosen-message attacks (EUF-CMA) in the random oracl…

Sorrel: A simple and flexible framework for multi-agent reinforcement learning
Rebekah A. Gelp\'i, Yibing Ju, Ethan C. Jackson, Yikai Tang, Shon Verch, Claas Voelcker, William A. Cunningham
https://arxiv.org/abs/2506.00228

Sorrel: A simple and flexible framework for multi-agent reinforcement learning
We introduce Sorrel (https://github.com/social-ai-uoft/sorrel), a simple Python interface for generating and testing new multi-agent reinforcement learning environments. This interface places a high degree of emphasis on simplicity and accessibility, and uses a more psychologically intuitive structure for the basic agent-environment loop, making it a useful tool for social scientists to investigate how learning and social interaction leads to the development and change of group dynamics. In thi…

Enhancing Multi-Agent Collaboration with Attention-Based Actor-Critic Policies
Hugo Garrido-Lestache, Jeremy Kedziora
https://arxiv.org/abs/2507.22782 https://

Enhancing Multi-Agent Collaboration with Attention-Based Actor-Critic Policies
This paper introduces Team-Attention-Actor-Critic (TAAC), a reinforcement learning algorithm designed to enhance multi-agent collaboration in cooperative environments. TAAC employs a Centralized Training/Centralized Execution scheme incorporating multi-headed attention mechanisms in both the actor and critic. This design facilitates dynamic, inter-agent communication, allowing agents to explicitly query teammates, thereby efficiently managing the exponential growth of joint-action spaces while …

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
Weitao Zhou, Bo Zhang, Zhong Cao, Xiang Li, Qian Cheng, Chunyang Liu, Yaqin Zhang, Diange Yang
https://arxiv.org/abs/2506.16720

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
With the increasing presence of automated vehicles on open roads under driver supervision, disengagement cases are becoming more prevalent. While some data-driven planning systems attempt to directly utilize these disengagement cases for policy improvement, the inherent scarcity of disengagement data (often occurring as a single instances) restricts training effectiveness. Furthermore, some disengagement data should be excluded since the disengagement may not always come from the failure of dri…

RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms
Ziyao Wang, Rongpeng Li, Sizhao Li, Yuming Xiang, Haiping Wang, Zhifeng Zhao, Honggang Zhang
https://arxiv.org/abs/2507.01378

RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms
Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in numerical communication and the rigidity of homogeneous role structures, resulting in poor generalizat…