Good Papers

Showing Multi-agent RL Show all papers

65%Worth a look
?Worth a lookVote to see the score
arXivMulti-agent RL

Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies

OMAF proposes a one-step flow policy framework for online multi-agent reinforcement learning, achieving up to 3.4x higher returns and 10.5x sample efficiency over baselines.

Zhuoran Li, Yunzhan Li, Xun Wang, Yihan Du and 1 more

Published Oct 1, 2026 · 0 citations

0% Readers0 of 1 upvoted
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5
54%Worth a look
?Worth a lookVote to see the score
Institute of Computing TechnologU Chinese Academy of SciencesTencentMulti-agent RL

Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning

Rhombus uses reinforcement learning to incentivize coordination in parallel thinking frameworks.

Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu and 8 more

Published 2026 · 0 citations

100% Readers1 of 1 upvoted
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
Chapman & Hall/CRC eBooks 2025Multi-agent RL

Multi-Agent RL (MARL) Algorithms

The chapter explains multi-agent reinforcement learning algorithms by generalizing single-agent methods via reward machines.

Vinod K. Mishra

Published Oct 28, 2025 · 0 citations

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
88%Must read
?Must readVote to see the score
NeurIPS 2025WorkshopU EdinburghThe University of Texas at AustiMulti-agent RL

HyperMARL: Adaptive Hypernetworks for Multi-Agent RL

HyperMARL uses agent-conditioned hypernetworks to generate agent-specific parameters that decouple gradients, reducing variance and preserving behavioral diversity across multi-agent benchmarks without added complexity.

Kale-ab Tessera, Arrasy Rahman, Amos Storkey, Stefano Albrecht

Published 2025 · 0 citations

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 1/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Shanxishanxi universitySchool of Computer and InformatiMulti-agent RL

Adaptive Communication Range for Scalable Cooperative Multi-Agent Reinforcement Learning

Huizhong Song, Wei Wei, Lin Li, Lijun Zhang and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026CornellMulti-agent RL

Decentralized Q-Learning in Markov Potential Games

Onur Ünlü, Eilyan Bitar, Francesca Parise

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Georgia Institute of TechnologyGeorgia TechMulti-agent RL

Coupling-Aware Reinforcement Learning for Co-Evolving Graph Games

Mina Kim, Guanghui Lan, Benoit Montreuil

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026ShandongMulti-agent RL

Structural Entropy Optimized Communication for Multi-Agent Reinforcement Learning

Wei Du, Benyu Wu, Wei Guo, Zhongmin Yan and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U StuttgartU OxfordMulti-agent RL

High Entropy Regularization Leads to Symmetry Equivariant Policies in Dec-POMDPs

Johannes Forkel, Constantin Ruhdorfer, Michael Beukman, Andreas Bulling and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026KyotoGraduate School of Informatics, Multi-agent RL

Scalable Multi-Agent Contrastive Reinforcement Learning

Victor Augusto Kich, Satoshi Yamamori, Jun Morimoto

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Paul G. Allen School of ComputerU WashingtonU Washington, Google DeepMindMulti-agent RL

Why Copy Others? Insights into Social Learning from Multi-Agent Reinforcement Learning

Yancheng Liang, Shakti Senthil, Daphne Chen, Simon Du and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026SUN YAT-SEN UNIVERSITYSun Yat-senMulti-agent RL

Policy-Level Exploration for Coordinated Multi-Agent Reinforcement Learning

Yucong Zhang, Chao Yu

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U MacauShenzhenU macauMulti-agent RL

CorridorLight: Cooperation as Task Negotiation with Causal Gating for Traffic Signal Control

Pak Lon Ip, Pengfei Ren, Yuteng Lin, Rongqin Chen and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Huazhong AgriculturalHuawei Technologies Co., Ltd.U BristolNational University of Defense TWuhan TextileMulti-agent RL

From Local to Global: Progressive Consensus via Hierarchical Communication in Multi-Agent Reinforcement Learning

Jiangjin Yin, Zhonglin Lv, Hangyu Mao, Rongbo Zhu and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026INRIAEcole Nationale de la StatistiquInriaMulti-agent RL

DynamAuction: a reinforcement learning environment for repeated auction with dynamic value

Benjamin Heymann, Eugénie Patard, Corentin Pla, Patrick Loiseau

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Johns HopkinsNew Jersey Institute of TechnoloMulti-agent RL

Policy Regret Minimization in Partially Observable Markov Games

Lan Sang, Raman Arora, Thanh Nguyen-Tang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026ZhejiangMulti-agent RL

Toward Online Robust Zero-Sum Markov Games with Function Approximation

Chenhao Zhou, Jiyu Wei

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U FloridaTianjin NormalMulti-agent RL

Know Your Task, Learn It Right: Task-Aware Optimistic Value Learning for Multi-Task Multi-Agent Reinforcement Learning

Chang Liu, Mengyang Li

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026NorthwesternTiktokU ChicagoUC BerkeleyNVIDIAMulti-agent RL

Is Decentralized LLM Agent RL Robust to Heterogeneity? An Asymmetric Tale

Canyu Chen, Kangyu Zhu, Zhaorun Chen, Zhanhui Zhou and 5 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 1/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026OralInstitute of automation, ChineseInstitute of automation, ChineseU the Chinese Academy of ScienceU the Chinese Academy of ScienceMulti-agent RL

Coordination Connectivity: Shared Initialization Shapes the Joint-Policy Landscape in MARL

Yixiang Fan, Zhiqiang Pu, Hao Ma, Dongmin Li and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Seoul NationalROBROSMulti-agent RL

Cooperative Multi-Agent Reinforcement Learning via Epigraph-Form Guided Exploration

Sungil Son, Hoseong Jung, Dahyun Oh, H. Jin Kim

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026The Hong Kong University of ScieNational University of SingaporeShanghai Jiao TongMulti-agent RL

Improved Sample Complexity for Markov Games via Variance-Aware Bandit Learning

Hanbin Zhou, Canzhe Zhao, Shuai Li

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026International Institute of InforInternational Institute of InforInternational Institute of InforInternational Institute of InforMulti-agent RL

Safe-Fair MACPO: Burden-Fair Constrained Policy Optimization for Safe Multi-Agent Reinforcement Learning

Ankita Kushwaha, KIRAN RAVISH, Preeti, Pawan Kumar

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026PurdueAppleDEVCOM Army Research LaboratoryMulti-agent RL

StarCraft Motion: A Dataset for Agent Simulation in Adversarial and Partially Observable Scenarios

Yi-Chung Chen, Mingyu Kim, Ruqi Bai, James Z Hare and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U StellenboschInstaDeepU Cape Town & InstaDeepInstaDeep LtdInstadeep LtdMulti-agent RL

Coordinating Hundreds of RL Agents through Scalable Inference-Time Search

Daniel Rajaonarivonivelomanantsoa, Oussama Hidaoui, Refiloe Shabe, Noah De Nicola and 12 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Xi'an JiaotongMulti-agent RL

Interaction Value Inference for Multi-Agent Reinforcement Learning via a Hierarchical Agent-Centric World Model

Zhuoran Chen, Xuyang Lu, Zeyang Liu, Xinrui Yang and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026East China University of ScienceU PaduaHumboldt Universität BerlinMulti-agent RL

Submodular Multi-Agent Reinforcement Learning for Effective Online Distributed Task Allocation

Jing Liu, Yangyang YANG, Luca Ballotta, Fangfei Li and 2 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Huazhong AgriculturalHuawei Technologies Co., Ltd.School of Artificial IntelligencMulti-agent RL

Semantic-level Exploration for Multi-Agent Reinforcement Learning

Jiangjin Yin, Zijian Ye, Rongbo Zhu, Hangyu Mao and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Institute of Computing Technolog, Chinese Academy of SciencesU the Chinese Academy of ScienceMulti-agent RL

DDACOM: Dual-Decoupled Adaptive Communication for Multi-Agent Reinforcement Learning under Dynamic Networks

Yiqun Wu, QI WANG, Mengxian Li, Yongjun Xu

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Xi‘an JiaotongXi'an JiaotongMulti-agent RL

CHAIN: Continual Heterogeneous Cooperation with Information Bottleneck for Multi-Agent Reinforcement Learning

Haowen Dou, Lujuan Dang, Badong Chen

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U FloridaMulti-agent RL

Mind the Gap: Information Disadvantage as a Learning Signal in Cooperative MARL

Chang Liu

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Arizona StateU Central FloridaMulti-agent RL

CAFE: Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning

Arun Vignesh Malarkkan, Wangyang Ying, Hongyu Cao, Dongjie Wang and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U Texas at AustinU AlbertaTexas A&M University - ColleKAISTMulti-agent RL

Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL

Dongsu Lee, Sooraj Sathish, Joonkyung Kim, Woojun Kim and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U Science and Technology of ChinInstitute of Artificial IntelligU Science and Technology of ChinMulti-agent RL

Joint Adaptive Neighborhood Constraint for Offline Multi-Agent Reinforcement Learning

Xiancheng Gao, Mingxiao Feng, Lin Liu, Yuanrui Duan and 3 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026NVIDIATechnionMulti-agent RL

Split-RL: Local Conflict Resolution in Reinforcement Learning

Benjamin Fuhrer, Chen Tessler, Gal Dalal

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026ColumbiaMulti-agent RL

Learning in Causal Markov Games

Aurghya Maiti, Elias Bareinboim

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U BristolShanghai JiaotongELLIS Institute FinlandMulti-agent RL

Axiomatic Reinforcement Learning for Open Multi-Agent Systems from Shapley Axioms

Jianhong Wang, Yang Li, Samuel Kaski, Jonathan Lawry

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Ulsan National Institute of ScieUNISTMulti-agent RL

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning

Yisak Park, Sunwoo Lee, Seungyul Han

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026California Institute of TechnoloJohns HopkinsCaltechMulti-agent RL

Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies

Yizhou Zhang, Yashaswini Murthy, Laixi Shi, Adam Wierman

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Korea Advanced Institute of ScieKAISTMulti-agent RL

Rethinking Credit Assignment in Cooperative MARL via Interventional Reward Response

Chamjin Joo, Myounghoon Ha, Sang Wan Lee

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
Show 20 more papers