arXiv AI

Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL

arXiv:2307. 01472v2 Announce Type: replace Abstract: We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL).

arXiv AI

Jun 11

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.

By Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang

agents diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Sep 1

Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management

arXiv:2608.28878v1 Announce Type: cross Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...

By Yiming Zhang, Kun Yang, Cong Shen, Dongning Guo

llms agents reinforcement-learning fine-tuning

arXiv AI

Sep 4

Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning

The paper investigates zero‑shot task generalisation in offline multi‑agent reinforcement learning by extending sequence‑modeling architectures to support multi‑task observation and action spaces and variable agent counts. It finds that increasing task diversity, rather than merely enlarging the dataset, is the key driver for robust zero‑shot transfer. Experiments on four challenging environments show a 3.2× mean improvement on held‑out tasks compared to single‑task models and outperform strong behaviour‑cloning baselines.

By Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob, Siddarth Singh, Juan Claude Formanek, Felix Chalumeau, Omayma Mahjoub, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Louay Ben Nessir, Simon Verster Du Toit, Daniel Rajaonarivonivelomanantsoa, Asim Awad Osman, Arnol Manuel Fokam, Refiloe Shabe, Arnu Pretorius

agents reinforcement-learning

arXiv Machine Learning

Aug 6

State2State: Environment-Derived Mid-Training for LLM Agents

arXiv:2608. 04934v1 Announce Type: cross Abstract: Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers.

By Xuanyu Lei, Yiqi Zhu, Chenliang Li, Kaiming Liu, Peng Li, Ming Yan, Jieping Ye, Ya-Qin Zhang, Yang Liu

llms agents reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 4

Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling

arXiv:2606. 05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.

By Marc Walden, Jason Liu, Shaashwath Sivakumar, Ryan Liu, Hamza Khan

agents reinforcement-learning benchmarks

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 Machine Learning

Jun 16

FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies

arXiv:2603. 27450v2 Announce Type: replace Abstract: Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation.

By Chenxiao Gao, Edward Chen, Tianyi Chen, Bo Dai

diffusion reinforcement-learning robotics efficiency benchmarks

arXiv Computer Vision

Sep 21

MintAct: A Unified Visual Agent for Digital Environments

arXiv:2609.22083v1 Announce Type: new Abstract: We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual too...

By Mingfei Gao, Rui Tian, Haiming Gang, Bohan Zhai, Le Zhang, Yuanzheng Gong, Di Feng, Ege \"Ozsoy, Kaixin Ma, Vishwesh Kirthivasan, O\u{g}uzhan Fatih Kar, Roman Bachmann, Anders Boesen Lindbo Larsen, Afshin Dehghan

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Jul 7

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.

By Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li, Kaiqiao Han, Chenyi Tong, Haoran Deng, Renliang Sun, Alexander Taylor, Yanqiao Zhu, Jason Cong, Yizhou Sun, Wei Wang

llms agents reinforcement-learning

arXiv Machine Learning

Jun 2

Mean Flow Policy Optimization

arXiv:2604. 14698v2 Announce Type: replace Abstract: Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL).

By Xiaoyi Dong, Xi Sheryl Zhang, Jian Cheng

diffusion reinforcement-learning benchmarks

arXiv AI

Sep 23

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making

arXiv:2605.01457v4 Announce Type: replace Abstract: How can generative offline multi-agent reinforcement learning achieve both fast joint trajectory generation and effective cooperation? Multi-agent...

By Guowei Zou, Haitao Wang, Beiwen Zhang, Boning Zhang, Hejun Wu

agents diffusion reinforcement-learning efficiency benchmarks

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