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REVIEW 4 major objections 5 minor 168 references

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey proposes adaptability as the unifying lens for judging whether multi-agent reinforcement learning algorithms survive shifting conditions, and divides it into learning adaptability, policy adaptability, and scenario-driven…

desk verdict Useful taxonomy, unreliable operationalization: Table 1 contradicts its own prose, but the framework deserves a serious referee. read the letter →

arxiv 2507.10142 v2 pith:YF2K6TIN submitted 2025-07-14 cs.AI cs.LGcs.MA

Siyi Hu , Mohamad A Hady , Jianglin Qiao , Jimmy Cao , Mahardhika Pratama , Ryszard Kowalczyk This is my paper
classification cs.AIcs.LGcs.MA
keywords multi-agentreinforcementlearningadaptabilitypolicyscenario-drivenzero-shotcoordinationofflineMARLbenchmarkevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that the reason multi-agent reinforcement learning (MARL) works in benchmarks but fails in deployments is that algorithms carry unstated assumptions about agent count, training access, synchronization, and partner behavior. The paper introduces adaptability as a single umbrella concept for what changes when a MARL system moves to a new setting, and splits it into three dimensions: learning adaptability (training-time resilience to population, task, and execution changes), policy adaptability (reuse of a trained policy across tasks, roles, and unfamiliar partners), and scenario-driven adaptability (whether benchmarks expose those shifts in a controlled way). The claim is that organizing existing work under these three headings clarifies how scalability, robustness, generalization, and transferability relate, and reveals where evaluation is underspecified. A sympathetic reader would take the paper's contribution to be the framework itself: a vocabulary and checklist for asking which assumption a MARL algorithm violates and what kind of adaptation is allowed.

What carries the argument

The central object is the three-dimensional adaptability taxonomy itself: learning adaptability, policy adaptability, and scenario-driven adaptability, each subdivided into axes such as population scaling, task structure, execution constraints, permutation invariance, offline-to-online transfer, and zero-shot coordination. Its work is to act as a classification scheme: every MARL paradigm and benchmark is assigned a position on these axes, and the paper's tables turn implicit assumptions into explicit, comparable entries. The framework also supplies four clarifying questions—what changes, when the change occurs, what adaptation is allowed, and what success means—which the paper uses to separate concepts that previous reviews conflate.

What would settle it

Take a single MARL algorithm, hold its architecture fixed, and construct two environments that differ only in whether a population change happens during training or during deployment. If the algorithm's failure mode is identical in both, with the same coordination breakdown and the same performance drop curve, then the division between learning and policy adaptability is not separating what it claims to separate, and the three dimensions are not independent.

Watch

Extended reading notes

Core claim

The paper's central claim is that adaptability, defined as any change in environment dynamics during learning or execution, can serve as a unified and practically grounded lens for evaluating MARL reliability, and that this lens decomposes into three separable dimensions. Learning adaptability asks whether the learning paradigm itself remains stable when agent populations, task structures, or execution constraints shift. Policy adaptability asks whether a policy trained on one configuration can generalize or transfer to new tasks, roles, or partners without retraining. Scenario-driven adaptability asks whether benchmarks and evaluation protocols expose controlled, diagnostic shifts. The paper then applies this taxonomy across paradigms, across policy mechanisms, and across benchmarks, producing tables that classify each approach's suitability along the three axes. The intended contribution is unification: existing notions such as scalability, robustness, and transferability become facets of adaptability, and their intersections mark where real-world readiness is actually tested.

Load-bearing premise

The load-bearing premise is that changes in learning conditions, deployment conditions, and evaluation scenarios can be cleanly separated into three independent dimensions; if real shifts straddle or transform one another, as when asynchronous execution reshapes population scale or offline data encodes partner conventions, the taxonomy will misclassify or double-count the same phenomenon.

Editorial extensions

If this is right

  • If the taxonomy is adopted, evaluation of MARL algorithms will routinely report which adaptability dimension is being tested, rather than relying on a single aggregate score.
  • Existing properties like scalability, robustness, and transferability become partial facets of adaptability; a method that is scalable but not policy-adaptable is located on the framework rather than praised or dismissed wholesale.
  • Benchmark designers gain concrete design principles, such as incremental population scaling, role diversification, reward consistency, and held-out partner diversity, for making environments diagnostically useful.
  • Offline-to-online transfer and zero-shot coordination are identified as under-specified axes where both algorithms and benchmarks lag, which points future work toward those gaps.
  • The classification predicts that no single existing paradigm covers all three dimensions natively; independent learning is most flexible at execution, centralized methods at coordination, and neither at cross-task generalization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy's three dimensions likely interact more than the paper's presentation suggests; for example, asynchronous execution can change effective population scale, and offline data can encode partner conventions, so a single algorithm may be classifiable on two axes simultaneously.
  • A testable extension is an adaptability 'scorecard' that forces each study to state the violated assumption, the dimension, the allowed adaptation budget, and the success metric, a card that could be extracted directly from the tables proposed here.
  • The framework could be applied retroactively to single-agent RL, where policy adaptability and scenario-driven adaptability are already studied but learning adaptability under population scaling disappears, suggesting adaptability is really a continuum rather than MARL-specific.
  • Building a benchmark suite that varies each of the three dimensions independently while measuring performance degradation would empirically validate or refute the separability assumption that the taxonomy rests on.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes adaptability as an assumption-aware lens for evaluating MARL algorithms under shifting conditions, organized into three dimensions: learning adaptability (training/paradigm robustness), policy adaptability (reuse and generalization of a single policy), and scenario-driven adaptability (benchmark and evaluation design). It reviews classical paradigms (CTDE, independent learning, offline, mean-field, networked, model-based, safe MARL), five families of policy-generalization methods, and a broad set of structured-game, simulator, and LLM-based benchmarks. Two summary tables operationalize the framework: Table 1 rates learning adaptability of paradigms across seven axes, and Table 2 characterizes environment configurability. The central claim is that this taxonomy is a valid, practically grounded way to organize MARL research and to obtain reliable classifications of algorithms and benchmarks.

Significance. If the inconsistencies are repaired, the framework is a useful conceptual contribution: it gives a shared vocabulary for concepts such as scalability, robustness, generalization, and transferability that are often used inconsistently, it separates learning-time from deployment-time questions in a principled way, and it collates a wide literature with specific pointers to algorithms and benchmarks. The paper's explicit key questions and the benchmark table (Table 2) are valuable resources. The main value is organizational rather than novel technical results, which is appropriate for a survey. The weakness is that the framework's own operationalization in Table 1 is not yet reliable, so the central claim is conditional on a revision.

major comments (4)
  1. [Table 1 vs. Section 3.2] The table and prose give contradictory classifications. Table 1 rates Independent Learning's Competitive entry as natively suitable, while Section 3.2 says IL is 'usable only in restricted forms and typically lack mechanisms for anticipating adversarial strategies'; Centralized Critic's Competitive entry is natively suitable, while Section 3.2 says CTDE methods are 'fundamentally misaligned with competitive settings'; and Networked MARL's Cooperative entry is incompatible, while Section 3.2 says networked architectures 'support effective cooperation in large-scale settings.' Since the paper's central claim is that the three-dimensional taxonomy yields reliable classifications, these direct contradictions are load-bearing and must be resolved by aligning the table with the prose or by revising the prose.
  2. [Section 3.3 final paragraph] The sentence 'As shown in Table 1, IL and networked MARL offer the greatest flexibility for distributed, asynchronous deployment' is not what Table 1 shows: Networked MARL has Distributed Training = ✓ but Asynchronous Execution = ✗. The summary overstates the table and should either be corrected or the table's Async column for Networked MARL should be changed to a partial rating with a justification in the text.
  3. [Table 1, Async. Exec. column] The rating for Offline MARL (△) is justified in Section 3.3 by deployment-time inference behavior, but Table 1 is presented as a dimension of Learning Adaptability. This mixes training-phase and deployment-phase criteria within a single column, which is exactly the kind of phase confusion the framework's learning/policy distinction was designed to avoid. Each row or column should be tagged with the phase to which the claim applies, or the column should be split into training-time and deployment-time asynchrony.
  4. [Section 3.2 and Table 1] No scoring rubric is given for the four-level suitability scale. In particular, IL's Cooperative = ✓ sits uneasily with Section 3.1's statement that IL methods 'typically struggle to learn coordinated behaviours in tightly coupled environments.' The authors should specify what evidence (benchmark results, architectural properties, or both) upgrades a paradigm to 'natively suitable' and how border cases map to 'partially suitable'; without such a rubric the table is not falsifiable.
minor comments (5)
  1. [Section 3.3] Section 3.3 contains the incomplete sentence 'These constraints' immediately before 'Distributed Training Support.'
  2. [Section 2.2] The phrase 'we introduce a structured framework describe MARL adaptability' should read 'a structured framework that describes MARL adaptability.'
  3. [Section 3.2] The sentence 'Competitive tasks involvedirectlyconflictinggoals' should be 'Competitive tasks involve directly conflicting goals.'
  4. [Section 4.4] The sentence 'Another approaches propose an auto-curriculum' should be 'Another approach proposes an auto-curriculum' or 'Other approaches propose an auto-curriculum.'
  5. [Section 3.3] The sentence 'CTDE methods, such as VD, CC, and HA, are only partially compatible' with distributed training is inconsistent with Table 1, which gives Value Decomposition Distributed Training = ✗; either the text or the table should be adjusted.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey reorganizes existing MARL literature under a qualitative taxonomy; no predictions are derived from fitted parameters or self-citation chains.

full rationale

This is a conceptual survey rather than a derivation. Its central contribution is a three-dimensional taxonomy (learning adaptability, policy adaptability, scenario-driven adaptability) used to re-organize existing MARL methods and benchmarks, and no equation, fitted parameter, or quantitative prediction is produced that could reduce to an input. The framework's classifications are literature-synthesis assertions supported by citations to external prior work, not by the taxonomy itself; for example, the claim that value-decomposition methods are natively suitable for cooperative tasks is anchored in the cited QMIX/VD literature rather than in the paper's own definitions. Self-citations (UPDeT, HAPPO, HASAC) appear only as representative examples within the review and are not load-bearing: removing them would not change any classification or conclusion. The paper's assumptions about the separability of the three axes are explicit in the Introduction but remain assumptions, not circular derivations. The noted inconsistencies between Section 3.2's prose and Table 1 (e.g., Table 1 rates Centralized Critic as natively suitable for Competitive while Section 3.2 states that CTDE methods are fundamentally misaligned with competitive settings) are internal-consistency issues rather than circularity, because the ratings are not derived from the framework by construction. Overall, the survey is self-contained as a taxonomical review and exhibits no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented physical entities are present. The framework is conceptual; the main borrowed assumptions are the value of adaptability as a lens, the completeness of the three-way split, and the accuracy of the subjective table ratings.

assumptions (3)
  • domain assumption Adaptability is a meaningful, distinct property worth measuring across MARL algorithms.
    The entire survey rests on the value judgment that adaptability is a useful organizing concept beyond scalability, robustness, generalization, and transferability; this is asserted in the Introduction and Abstract without empirical or formal justification.
  • ad hoc to paper The three dimensions (learning, policy, scenario-driven adaptability) form a complete and non-overlapping partition of relevant changes.
    This trichotomy is introduced by the authors (Sections 3, 4, 5) as the structure for the survey; no proof of exhaustiveness or mutual exclusivity is given, and the categories overlap (for example, offline-to-online transfer is discussed under both policy and scenario adaptability).
  • domain assumption Subjective ratings in Tables 1 and 2 accurately reflect the literature.
    The suitability ratings are assigned by the authors without a systematic review protocol, inter-rater reliability, or empirical evidence; this is visible in Table 1 and contradicted by the paper's own prose in Sections 3.1 and 3.2.

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Cite this review

Pith. "Pith review of Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review." pith.science (2026). https://pith.science/paper/YF2K6TIN

@misc{pith2026250710142,
  author       = {Pith},
  title        = {Pith review of: Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YF2K6TIN}},
  note         = {Machine review of arXiv:2507.10142}
}
read the original abstract

Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are designed and evaluated. Agent populations may change, objectives may shift, centralized information may be unavailable, execution may become asynchronous, and partner policies may be unfamiliar. Existing surveys discuss related desiderata such as scalability, robustness, generalization, and transferability, but these terms often refer to different objects of analysis and different kinds of distributional or structural shift. This survey proposes \textit{adaptability} as an assumption-aware taxonomy for organizing these shifts, rather than as a universal requirement that every MARL algorithm should succeed in every setting. We distinguish three dimensions: \textit{learning adaptability}, which concerns the applicability of learning paradigms under changed training or system assumptions; \textit{policy adaptability}, which concerns the reuse or adaptation of learned policies under deployment-time changes; and \textit{scenario-driven adaptability}, which concerns whether benchmarks and evaluation protocols expose controlled, diagnostically useful shifts. By separating what changes, when the change occurs, what adaptation is allowed, and what success means, the framework clarifies how established concepts fit together and identifies where current MARL evaluation remains underspecified.

Figures

Figures reproduced from arXiv: 2507.10142 by the authors.

Figure 1
Figure 1. Conceptual overlap between scalability, robustness, and generalization/transferability in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. A structured overview of MARL adaptability across three dimensions with key topics. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. MARL environments exhibit significant variability in agent population scale, ranging [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Illustration of four task modes in multi-agent systems categorized by reward structures: [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the key distinction between learning adaptability and policy adaptability. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Relationship between adaptable policies and available approaches. An adaptable policy [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

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Works this paper leans on

168 extracted references · 46 canonical work pages

  1. [1]

    A comprehensive survey of multia- gent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2):156–172, 2008

    Lucian Busoniu, Robert Babuska, and Bart De Schutter. A comprehensive survey of multia- gent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2):156–172, 2008

    2008

  2. [2]

    A survey of progress on cooperative multi-agent reinforcement learning in open environment

    Lei Yuan, Ziqian Zhang, Lihe Li, Cong Guan, and Yang Yu. A survey of progress on cooperative multi-agent reinforcement learning in open environment. arXiv preprint arXiv:2312.01058, 2023

    arXiv 2023

  3. [3]

    A survey of multi-agent deep reinforcement learning with communication.Autonomous Agents and Multi-Agent Systems, 38(1):4, 2024

    Changxi Zhu, Mehdi Dastani, and Shihan Wang. A survey of multi-agent deep reinforcement learning with communication.Autonomous Agents and Multi-Agent Systems, 38(1):4, 2024

    2024

  4. [4]

    Single and multi-agent deep reinforcement learning for ai-enabled wireless networks: A tutorial

    Amal Feriani and Ekram Hossain. Single and multi-agent deep reinforcement learning for ai-enabled wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 23(2):1226–1252, 2021

    2021

  5. [5]

    Multi-agent deep reinforcement learning for multi-robot applica- tions: A survey.Sensors, 23(7):3625, 2023

    James Orr and Ayan Dutta. Multi-agent deep reinforcement learning for multi-robot applica- tions: A survey.Sensors, 23(7):3625, 2023

    2023

  6. [6]

    Multi-agent deep reinforcement learning: a survey.Arti- ficial Intelligence Review, 55(2):895–943, 2022

    Sven Gronauer and Klaus Diepold. Multi-agent deep reinforcement learning: a survey.Arti- ficial Intelligence Review, 55(2):895–943, 2022

    2022

  7. [7]

    A survey on large-population systems and scalable multi-agent reinforcement learning

    KaiCui, AnamTahir, GizemEkinci, AhmedElshamanhory, YannickEich, MengguangLi, and Heinz Koeppl. A survey on large-population systems and scalable multi-agent reinforcement learning. arXiv preprint arXiv:2209.03859, 2022

    arXiv 2022

  8. [8]

    Multi-agent reinforcement learning: A selective overview of theories and algorithms.Handbook of reinforcement learning and control, pages 321–384, 2021

    Kaiqing Zhang, Zhuoran Yang, and Tamer Başar. Multi-agent reinforcement learning: A selective overview of theories and algorithms.Handbook of reinforcement learning and control, pages 321–384, 2021

    2021

Show all 168 references
  1. [9]

    MIT press Cambridge, 1998

    Richard S Sutton, Andrew G Barto, et al.Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998. 23

    1998

  2. [10]

    Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015

    2015

  3. [11]

    Deep reinforcement learning: A brief survey.IEEE Signal Processing Magazine, 34(6):26–38, 2017

    Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. Deep reinforcement learning: A brief survey.IEEE Signal Processing Magazine, 34(6):26–38, 2017

    2017

  4. [12]

    Multi-agent reinforcement learning: Independent vs

    Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. InPro- ceedings of the international conference on machine learning, pages 330–337, 1993

    1993

  5. [13]

    Mean field multi-agent reinforcement learning

    Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. Mean field multi-agent reinforcement learning. InInternational conference on machine learning, pages 5571–5580. PMLR, 2018

    2018

  6. [14]

    Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning

    Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning. InProceedings of the International Conference on Machine Learning, pages 4295...

    2018

  7. [15]

    Partially observable mean field reinforcement learning

    Sriram Ganapathi Subramanian, Matthew E Taylor, Mark Crowley, and Pascal Poupart. Partially observable mean field reinforcement learning. InProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pages 537–545, 2021

    2021

  8. [16]

    Rode: Learning roles to decompose multi-agent tasks

    Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, and Chongjie Zhang. Rode: Learning roles to decompose multi-agent tasks. InInternational Conference on Learning Representations, 2021

    2021

  9. [17]

    Heterogeneous-agent mirror learning: A continuum of solutions to cooperative marl.arXiv preprint arXiv:2208.01682, 2022

    Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding, Hao Dong, Jun Wang, and Yaodong Yang. Heterogeneous-agent mirror learning: A continuum of solutions to cooperative marl.arXiv preprint arXiv:2208.01682, 2022

    2022 arXiv

  10. [18]

    The surprising effectiveness of PPO in cooperative multi-agent games

    Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. The surprising effectiveness of PPO in cooperative multi-agent games. InAdvances in Neural Information Processing Systems, pages 24611–24624, 2022

    2022

  11. [19]

    Trust region policy optimisation in multi-agent reinforcement learning

    JG Kuba, R Chen, M Wen, Y Wen, F Sun, J Wang, and Y Yang. Trust region policy optimisation in multi-agent reinforcement learning. InICLR 2022-10th International Con- ference on Learning Representations, page 1046. The International Conference on Learning Representations (ICLR), 2022

    2022

  12. [20]

    Learning mean-field games.Advances in neural information processing systems, 32, 2019

    Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang. Learning mean-field games.Advances in neural information processing systems, 32, 2019

    2019

  13. [21]

    Believewhatyousee: Implicitconstraintapproachforofflinemulti-agent reinforcement learning.Advances in Neural Information Processing Systems, 34:10299–10312, 2021

    Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, andQianchuanZhao. Believewhatyousee: Implicitconstraintapproachforofflinemulti-agent reinforcement learning.Advances in Neural Information Processing Systems, 34:10299–10312, 2021

    2021

  14. [22]

    Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification

    Ling Pan, Longbo Huang, Tengyu Ma, and Huazhe Xu. Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification. InInternational conference on machine learning, pages 17221–17237. PMLR, 2022. 24

    2022

  15. [23]

    Off-the-grid marl: Datasets and baselines for offline multi-agent reinforcement learning

    Claude Formanek, Asad Jeewa, Jonathan Shock, and Arnu Pretorius. Off-the-grid marl: Datasets and baselines for offline multi-agent reinforcement learning. InExtended Abstract at the 2023 International Conference on Autonomous Agents and Multiagent Systems. AAMAS, 2023

    2023

  16. [24]

    Networked multi-agent reinforcement learn- ing in continuous spaces

    Kaiqing Zhang, Zhuoran Yang, and Tamer Basar. Networked multi-agent reinforcement learn- ing in continuous spaces. In 2018 IEEE conference on decision and control (CDC), pages 2771–2776. IEEE, 2018

    2018

  17. [25]

    Fully decentralized multi-agent reinforcement learning with networked agents

    Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, and Tamer Basar. Fully decentralized multi-agent reinforcement learning with networked agents. In International conference on machine learning, pages 5872–5881. PMLR, 2018

    2018

  18. [26]

    Graph convolutional reinforce- ment learning.arXiv preprint arXiv:1810.09202, 2018

    Jiechuan Jiang, Chen Dun, Tiejun Huang, and Zongqing Lu. Graph convolutional reinforce- ment learning.arXiv preprint arXiv:1810.09202, 2018

    2018 arXiv

  19. [27]

    Mambpo: Sample-efficient multi-robot reinforcement learning using learned world models

    Daniël Willemsen, Mario Coppola, and Guido CHE de Croon. Mambpo: Sample-efficient multi-robot reinforcement learning using learned world models. In2021 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pages 5635–5640. IEEE, 2021

    2021

  20. [28]

    Mingling foresight with imagination: Model-based cooperative multi-agent reinforcement learning.Advances in Neural Information Processing Systems, 35:11327–11340, 2022

    Zhiwei Xu, Bin Zhang, Yuan Zhan, Yunpeng Baiia, Guoliang Fan, et al. Mingling foresight with imagination: Model-based cooperative multi-agent reinforcement learning.Advances in Neural Information Processing Systems, 35:11327–11340, 2022

    2022

  21. [29]

    Scalablemulti-agentmodel-basedreinforcementlearn- ing

    VladimirEgorovandAlekseiShpilman. Scalablemulti-agentmodel-basedreinforcementlearn- ing. arXiv preprint arXiv:2205.15023, 2022

    2022 arXiv

  22. [30]

    Mamps: Safe multi-agent reinforcement learning via model predictive shielding.arXiv preprint arXiv:1910.12639, 2019

    Wenbo Zhang, Osbert Bastani, and Vijay Kumar. Mamps: Safe multi-agent reinforcement learning via model predictive shielding.arXiv preprint arXiv:1910.12639, 2019

    1910 arXiv

  23. [31]

    Cmix: Deep multi-agent reinforcement learning with peak and average constraints

    ChenyiLiu, NanGeng, VaneetAggarwal, TianLan, YuanYang, andMingweiXu. Cmix: Deep multi-agent reinforcement learning with peak and average constraints. InMachine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, Se...

    2021

  24. [32]

    Safe multi-agent reinforcement learning for multi-robot control

    Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang. Safe multi-agent reinforcement learning for multi-robot control. Artificial Intelligence, 319:103905, 2023

    2023

  25. [33]

    Multi- agent actor-critic for mixed cooperative-competitive environments

    Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. Multi- agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems, pages 6379–6390, 2017

    2017

  26. [34]

    Counterfactual multi-agent policy gradients

    Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon White- son. Counterfactual multi-agent policy gradients. InProceedings of the AAAI Conference on Artificial Intelligence, pages 2974–2982, 2018

    2018

  27. [35]

    Value-decomposition networks for cooperative multi-agent learning based on team reward

    Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, and Thore Graepel. Value-decomposition networks for cooperative multi-agent learning based on team reward. ...

    2018

  28. [36]

    {QPLEX}: Duplex dueling multi-agent q-learning

    Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang. {QPLEX}: Duplex dueling multi-agent q-learning. In International Conference on Learning Representations, 2021

    2021

  29. [37]

    Maximum entropy heterogeneous-agent reinforcement learning

    Jiarong Liu, Yifan Zhong, Siyi Hu, Haobo Fu, QIANG FU, Xiaojun Chang, and Yaodong Yang. Maximum entropy heterogeneous-agent reinforcement learning. InThe Twelfth Inter- national Conference on Learning Representations, 2024

    2024

  30. [38]

    Heterogeneous-agentreinforcementlearning

    Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, and Yaodong Yang. Heterogeneous-agentreinforcementlearning. Journal of Machine Learning Research, 25(32):1– 67, 2024

    2024

  31. [39]

    Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

    Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, and Bolei Zhou. Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning. IEEE transactions on pattern analysis and machine intelligence, 45(3):3461–3475, 2022

    2022

  32. [40]

    Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving, 11 2020

    Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ah...

    2020

  33. [41]

    Multi-agent reinforcement learning aided intelligent uav swarm for target tracking

    Zhaoyue Xia, Jun Du, Jingjing Wang, Chunxiao Jiang, Yong Ren, Gang Li, and Zhu Han. Multi-agent reinforcement learning aided intelligent uav swarm for target tracking. IEEE Transactions on Vehicular Technology, 71(1):931–945, 2021

    2021

  34. [42]

    Mean-field theory for scale-free random networks

    Albert-László Barabási, Réka Albert, and Hawoong Jeong. Mean-field theory for scale-free random networks. Physica A: Statistical Mechanics and its Applications, 272(1-2):173–187, 1999

    1999

  35. [43]

    Mean field games and mean field type control theory, volume 101

    Alain Bensoussan, Jens Frehse, Phillip Yam, et al. Mean field games and mean field type control theory, volume 101. Springer, 2013

    2013

  36. [44]

    Optimal control of partially observable markovian systems.Journal of The Franklin Institute, 280(5):367–386, 1965

    Masanao Aoki. Optimal control of partially observable markovian systems.Journal of The Franklin Institute, 280(5):367–386, 1965

    1965

  37. [46]

    Eric Steinberger. Pokerrl. https://github.com/TinkeringCode/PokerRL, 2019

    2019

  38. [47]

    Offline reinforcement learning with implicit q-learning

    Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. arXiv preprint arXiv:2110.06169, 2021

    2021 arXiv

  39. [48]

    Off-policy deep reinforcement learning with- out exploration

    Scott Fujimoto, David Meger, and Doina Precup. Off-policy deep reinforcement learning with- out exploration. In International conference on machine learning, pages 2052–2062. PMLR, 2019. 26

    2019

  40. [49]

    Conservative q-learning for offline reinforcement learning

    Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. Advances in neural information processing systems, 33:1179– 1191, 2020

    2020

  41. [50]

    A review of cooperative multi-agent deep rein- forcement learning.Applied Intelligence, 53(11):13677–13722, 2023

    Afshin Oroojlooy and Davood Hajinezhad. A review of cooperative multi-agent deep rein- forcement learning.Applied Intelligence, 53(11):13677–13722, 2023

    2023

  42. [51]

    A review of safe reinforcement learning: Methods, theory and applications.arXiv preprint arXiv:2205.10330, 2022

    Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, and Alois Knoll. A review of safe reinforcement learning: Methods, theory and applications.arXiv preprint arXiv:2205.10330, 2022

    2022 arXiv

  43. [52]

    Is independent learning all you need in the starcraft multi-agent challenge?arXiv preprint arXiv:2011.09533, 2020

    Christian Schroeder De Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip HS Torr, Mingfei Sun, and Shimon Whiteson. Is independent learning all you need in the starcraft multi-agent challenge?arXiv preprint arXiv:2011.09533, 2020

    2011 arXiv

  44. [53]

    A survey of learning in multiagent environments: Dealing with non-stationarity.arXiv preprint arXiv:1707.09183, 2017

    Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, and Enrique Munoz De Cote. A survey of learning in multiagent environments: Dealing with non-stationarity.arXiv preprint arXiv:1707.09183, 2017

    2017 arXiv

  45. [54]

    Bench- marking multi-agent deep reinforcement learning algorithms in cooperative tasks

    Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V Albrecht. Bench- marking multi-agent deep reinforcement learning algorithms in cooperative tasks. InThirty- fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2022

    2022

  46. [55]

    Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning

    Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International conference on machine learning, pages 5887–5896. PMLR, 2019

    2019

  47. [56]

    Weighted qmix: Ex- panding monotonic value function factorisation for deep multi-agent reinforcement learning

    Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson. Weighted qmix: Ex- panding monotonic value function factorisation for deep multi-agent reinforcement learning. Advances in neural information processing systems, 33:10199–10210, 2020

    2020

  48. [57]

    On the approximation of cooperative heterogeneous multi-agent reinforcement learning (marl) using mean field control (mfc).Journal of Machine Learning Research, 23(129):1–46, 2022

    Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, and Satish V Ukkusuri. On the approximation of cooperative heterogeneous multi-agent reinforcement learning (marl) using mean field control (mfc).Journal of Machine Learning Research, 23(129):1–46, 2022

    2022

  49. [58]

    Multi type mean field reinforcement learning

    Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E Taylor, and Nidhi Hegde. Multi type mean field reinforcement learning. InProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pages 411–419, 2020

    2020

  50. [59]

    Efficient model-based multi-agent mean- field reinforcement learning.Transactions on Machine Learning Research, 2021

    Barna Pásztor, Andreas Krause, and Ilija Bogunovic. Efficient model-based multi-agent mean- field reinforcement learning.Transactions on Machine Learning Research, 2021

    2021

  51. [60]

    Centralized model and explo- ration policy for multi-agent rl.arXiv preprint arXiv:2107.06434, 2021

    Qizhen Zhang, Chris Lu, Animesh Garg, and Jakob Foerster. Centralized model and explo- ration policy for multi-agent rl.arXiv preprint arXiv:2107.06434, 2021

    2021 arXiv

  52. [61]

    Model-based opponent modeling

    Xiaopeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, and Zongqing Lu. Model-based opponent modeling. Advances in Neural Information Processing Systems, 35:28208–28221, 2022

    2022

  53. [62]

    Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning

    Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Başar, and Lior Horesh. Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning. InProceedings of the AAAI conference on artificial intelligence, volume 35, pages 8767–8775, 2021. 27

    2021

  54. [63]

    Shield decentralization for safe multi-agent reinforcement learning

    Daniel Melcer, Christopher Amato, and Stavros Tripakis. Shield decentralization for safe multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 35:13367–13379, 2022

    2022

  55. [64]

    Scalable primal- dual actor-critic method for safe multi-agent rl with general utilities

    Donghao Ying, Yunkai Zhang, Yuhao Ding, Alec Koppel, and Javad Lavaei. Scalable primal- dual actor-critic method for safe multi-agent rl with general utilities. Advances in Neural Information Processing Systems, 36:36524–36539, 2023

    2023

  56. [65]

    Multiagent planning with factored MDPs

    Carlos Guestrin, Daphne Koller, and Ronald Parr. Multiagent planning with factored MDPs. In Proc. NIPS, volume 14, pages 1523–1530. MIT Press, 2001

    2001

  57. [66]

    Efficient solution algorithms for factored mdps.Journal of Artificial Intelligence Research, 19:399–468, 2003

    Carlos Guestrin, Daphne Koller, Ronald Parr, and Shobha Venkataraman. Efficient solution algorithms for factored mdps.Journal of Artificial Intelligence Research, 19:399–468, 2003

    2003

  58. [67]

    Coordinated reinforcement learning

    Carlos Guestrin, Michail Lagoudakis, and Ronald Parr. Coordinated reinforcement learning. In Proc. ICML, volume 2, pages 227–234, 2002

    2002

  59. [68]

    Sparse cooperative q-learning

    Jelle R Kok and Nikos Vlassis. Sparse cooperative q-learning. InProceedings of the twenty-first international conference on Machine learning, page 61, 2004

    2004

  60. [69]

    Kok and Nikos Vlassis

    Jelle R. Kok and Nikos Vlassis. Collaborative multiagent reinforcement learning by payoff propagation. J. Mach. Learn. Res., 7(65):1789–1828, 2006

    2006

  61. [70]

    Networked distributed pomdps: A synthesis of distributed constraint optimization and pomdps

    Ranjit Nair, Pradeep Varakantham, Milind Tambe, and Makoto Yokoo. Networked distributed pomdps: A synthesis of distributed constraint optimization and pomdps. InAAAI, volume 5, pages 133–139, 2005

    2005

  62. [71]

    Approximate solutions for factored dec-pomdps with many agents

    Frans A Oliehoek, Shimon Whiteson, Matthijs TJ Spaan, et al. Approximate solutions for factored dec-pomdps with many agents. InAAMAS, pages 563–570, 2013

    2013

  63. [72]

    Scalable reinforcement learning of localized policies for multi-agent networked systems

    Guannan Qu, Adam Wierman, and Na Li. Scalable reinforcement learning of localized policies for multi-agent networked systems. InLearning for Dynamics and Control, pages 256–266. PMLR, 2020

    2020

  64. [73]

    Multi-agent reinforcement learning in stochastic networked systems.Advances in neural information processing systems, 34:7825–7837, 2021

    Yiheng Lin, Guannan Qu, Longbo Huang, and Adam Wierman. Multi-agent reinforcement learning in stochastic networked systems.Advances in neural information processing systems, 34:7825–7837, 2021

    2021

  65. [74]

    Value function fac- torisation with hypergraph convolution for cooperative multi-agent reinforcement learning

    Yunpeng Bai, Chen Gong, Bin Zhang, Guoliang Fan, and Xinwen Hou. Value function fac- torisation with hypergraph convolution for cooperative multi-agent reinforcement learning. arXiv:2112.06771, 2021

    2021 arXiv

  66. [75]

    Efficient coop- eration strategy generation in multi-agent video games via hypergraph neural network

    Bin Zhang, Yunpeng Bai, Zhiwei Xu, Dapeng Li, and Guoliang Fan. Efficient coop- eration strategy generation in multi-agent video games via hypergraph neural network. arXiv:2203.03265, 2022

    2022 arXiv

  67. [76]

    Magent: Amany-agentreinforcementlearningplatformforartificialcollectiveintelligence

    Lianmin Zheng, Jiacheng Yang, Han Cai, Ming Zhou, Weinan Zhang, Jun Wang, and Yong Yu. Magent: Amany-agentreinforcementlearningplatformforartificialcollectiveintelligence. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018

    2018

  68. [77]

    Partially observable mean field reinforcement learning

    Sriram Ganapathi Subramanian, Matthew E Taylor, Mark Crowley, and Pascal Poupart. Partially observable mean field reinforcement learning. InProc. AAMAS, volume 20, pages 537–545, 2021. 28

    2021

  69. [78]

    Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning.arXiv:1910.12802, 2019

    René Carmona, Mathieu Laurière, and Zongjun Tan. Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning.arXiv:1910.12802, 2019

    1910 arXiv

  70. [79]

    Mean-field multi-agent reinforcement learning: A decentralized network approach.arXiv:2108.02731, 2021

    Haotian Gu, Xin Guo, Xiaoli Wei, and Renyuan Xu. Mean-field multi-agent reinforcement learning: A decentralized network approach.arXiv:2108.02731, 2021

    2021 arXiv

  71. [80]

    Swarm robotics: a review from the swarm engineering perspective.Swarm Intelligence, 7:1–41, 2013

    Manuele Brambilla, Eliseo Ferrante, Mauro Birattari, and Marco Dorigo. Swarm robotics: a review from the swarm engineering perspective.Swarm Intelligence, 7:1–41, 2013

    2013

  72. [81]

    Neural mmo 2.0: A massively multi-task addition to massively multi-agent learning.Advances in Neural Information Processing Systems, 36:50094–50104, 2023

    Joseph Suarez, David Bloomin, Kyoung Whan Choe, Hao Xiang Li, Ryan Sullivan, Nishaanth Kanna, Daniel Scott, Rose Shuman, Herbie Bradley, Louis Castricato, et al. Neural mmo 2.0: A massively multi-task addition to massively multi-agent learning.Advances in Neural Information Pr...

    2023

  73. [82]

    Bsk-rl: Modular, high-fidelity reinforcement learning environments for spacecraft tasking

    Mark A Stephenson and Hanspeter Schaub. Bsk-rl: Modular, high-fidelity reinforcement learning environments for spacecraft tasking. In75th International Astronautical Congress, Milan, Italy, IAF, 2024

    2024

  74. [83]

    Multi-agent reinforcement learning for active voltage control on power distribution networks.Advances in Neural Information Processing Systems, 34:3271–3284, 2021

    Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, and Tim C Green. Multi-agent reinforcement learning for active voltage control on power distribution networks.Advances in Neural Information Processing Systems, 34:3271–3284, 2021

    2021

  75. [84]

    Updet: Universal multi-agent rl via policy decoupling with transformers

    Siyi Hu, Fengda Zhu, Xiaojun Chang, and Xiaodan Liang. Updet: Universal multi-agent rl via policy decoupling with transformers. InInternational Conference on Learning Represen- tations, 2021

    2021

  76. [85]

    Randomized entity-wise factorization for multi-agent reinforcement learning

    Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, and Fei Sha. Randomized entity-wise factorization for multi-agent reinforcement learning. In International Conference on Machine Learning, pages 4596–4606. PMLR, 2021

    2021

  77. [86]

    Boosting multiagent reinforcement learning via permutation invariant and permutation equivariant networks

    HAO Jianye, Xiaotian Hao, Hangyu Mao, Weixun Wang, Yaodong Yang, Dong Li, Yan Zheng, and Zhen Wang. Boosting multiagent reinforcement learning via permutation invariant and permutation equivariant networks. In The Eleventh International Conference on Learning Representations, 2022

    2022

  78. [87]

    The starcraft multi-agent challenge.arXiv preprint arXiv:1902.04043, 2019

    Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson. The starcraft multi-agent challenge.arXiv preprint arXiv:1902.04043, 2019

    1902 arXiv

  79. [88]

    Cityflow: Amulti-agentreinforcementlearning environment for large scale city traffic scenario

    Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Weinan Zhang, YongYu, HaimingJin, andZhenhuiLi. Cityflow: Amulti-agentreinforcementlearning environment for large scale city traffic scenario. InThe world wide web conference, pages 3620– 3624, 2019

    2019

  80. [89]

    Google research football: A novel reinforcement learning environment

    Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al. Google research football: A novel reinforcement learning environment. InProceedings of the AAAI conference on ...

    2020

  81. [90]

    Shaq: Incorporating shap- ley value theory into multi-agent q-learning

    Jianhong Wang, Yuan Zhang, Yunjie Gu, and Tae-Kyun Kim. Shaq: Incorporating shap- ley value theory into multi-agent q-learning. InAdvances in Neural Information Processing Systems, volume 35, pages 5941–5954, 2022. 29

    2022

  82. [91]

    Learning correlated communication topology in multi-agent reinforcement learning

    Yali Du, Bo Liu, Vincent Moens, Ziqi Liu, Zhicheng Ren, Jun Wang, Xu Chen, and Haifeng Zhang. Learning correlated communication topology in multi-agent reinforcement learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, page...

    2021

  83. [92]

    Facmac: Factored multi-agent centralised policy gradients

    Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Böhmer, and Shimon Whiteson. Facmac: Factored multi-agent centralised policy gradients. InAdvances in Neural Information Processing Systems, pages 12208–12221, 2021

    2021

  84. [93]

    Shapley q-value: A local reward approach to solve global reward games

    Jianhong Wang, Yuan Zhang, Tae-Kyun Kim, and Yunjie Gu. Shapley q-value: A local reward approach to solve global reward games. InProceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7285–7292, 2020

    2020

  85. [94]

    Nucleolus credit assignment for effective coalitions in multi-agent reinforcement learning.arXiv preprint arXiv:2503.00372, 2025

    Yugu Li, Zehong Cao, Jianglin Qiao, and Siyi Hu. Nucleolus credit assignment for effective coalitions in multi-agent reinforcement learning.arXiv preprint arXiv:2503.00372, 2025

    2025 arXiv

  86. [95]

    Model-free mean-field reinforcement learning: mean-field mdp and mean-field q-learning

    René Carmona, Mathieu Laurière, and Zongjun Tan. Model-free mean-field reinforcement learning: mean-field mdp and mean-field q-learning. The Annals of Applied Probability, 33(6B):5334–5381, 2023

    2023

  87. [96]

    Learning to communicate with deep multi-agent reinforcement learning.Advances in neural information processing systems, 29, 2016

    JakobFoerster, IoannisAlexandrosAssael, NandoDeFreitas, andShimonWhiteson. Learning to communicate with deep multi-agent reinforcement learning.Advances in neural information processing systems, 29, 2016

    2016

  88. [97]

    Learning multiagent communication with backprop- agation

    Sainbayar Sukhbaatar, Rob Fergus, et al. Learning multiagent communication with backprop- agation. Advances in neural information processing systems, 29, 2016

    2016

  89. [98]

    Learning structured communication for multi-agent reinforcement learning

    Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, and Hongyuan Zha. Learning structured communication for multi-agent reinforcement learning. Autonomous Agents and Multi-Agent Systems, 36(2):50, 2022

    2022

  90. [99]

    Model-based multi-agent policy optimization with adaptive opponent-wise rollouts.arXiv preprint arXiv:2105.03363, 2021

    Weinan Zhang, Xihuai Wang, Jian Shen, and Ming Zhou. Model-based multi-agent policy optimization with adaptive opponent-wise rollouts.arXiv preprint arXiv:2105.03363, 2021

    2021 arXiv

  91. [100]

    Offline pre-trained multi-agent decision transformer

    Linghui Meng, Muning Wen, Chenyang Le, Xiyun Li, Dengpeng Xing, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, Yaodong Yang, et al. Offline pre-trained multi-agent decision transformer. Machine Intelligence Research, 2023

    2023

  92. [101]

    Hgap: boosting permutation invariant and permutation equivariant in multi-agent reinforcement learning via graph attention network

    Bor-Jiun Lin and Chun-Yi Lee. Hgap: boosting permutation invariant and permutation equivariant in multi-agent reinforcement learning via graph attention network. InForty-first International Conference on Machine Learning, 2024

    2024

  93. [102]

    M3: Modularization for multi-task and multi-agent offline pre-training

    Linghui Meng, Jingqing Ruan, Xuantang Xiong, Xiyun Li, Xi Zhang, Dengpeng Xing, and Bo Xu. M3: Modularization for multi-task and multi-agent offline pre-training. InProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

    2023

  94. [103]

    Learning generalizable skills from offline multi-task data for multi-agent cooperation

    Sicong Liu, Yang Shu, Chenjuan Guo, and Bin Yang. Learning generalizable skills from offline multi-task data for multi-agent cooperation. InInternational Conference on Learning Representations, 2025. 30

    2025

  95. [104]

    Maskma: Towards zero-shot multi-agent decision making with mask-based collaborative learning.Transactions on Machine Learning Research, 2023

    Jie Liu, Yinmin Zhang, Chuming Li, Zhiyuan You, Zhanhui Zhou, Chao Yang, Yaodong Yang, Yu Liu, and Wanli Ouyang. Maskma: Towards zero-shot multi-agent decision making with mask-based collaborative learning.Transactions on Machine Learning Research, 2023

    2023

  96. [105]

    Decision transformer: Reinforcement learning via sequence modeling

    Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084–15097, 2021

    2021

  97. [106]

    Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

    Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

    2017

  98. [107]

    Learning task embeddings for teamwork adaptation in multi-agent reinforcement learning

    Lukas Schafer, Filippos Christianos, Amos Storkey, and Stefano Albrecht. Learning task embeddings for teamwork adaptation in multi-agent reinforcement learning. InNeurIPS 2023 Workshop on Generalization in Planning, 2023

    2023

  99. [108]

    Multi-task multi-agent reinforcement learning with interaction and task representations

    Chao Li, Shaokang Dong, Shangdong Yang, Yujing Hu, Tianyu Ding, Wenbin Li, and Yang Gao. Multi-task multi-agent reinforcement learning with interaction and task representations. IEEE Transactions on Neural Networks and Learning Systems, 2024

    2024

  100. [109]

    Decompose a task into generalizable subtasks in multi-agent reinforcement learning

    Zikang Tian, Ruizhi Chen, Xing Hu, Ling Li, Rui Zhang, Fan Wu, Shaohui Peng, Jiaming Guo, Zidong Du, Qi Guo, et al. Decompose a task into generalizable subtasks in multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 2023

    2023

  101. [110]

    Trajectory-class-aware multi- agent reinforcement learning

    Hyungho Na, Kwanghyeon Lee, Sumin Lee, and Il-Chul Moon. Trajectory-class-aware multi- agent reinforcement learning. InThe Thirteenth International Conference on Learning Rep- resentations, 2025

    2025

  102. [111]

    Multi-task multi-agent reinforcement learning with task-entity transformers and value decomposition training

    Yuanheng Zhu, Shangjing Huang, Binbin Zuo, Dongbin Zhao, and Changyin Sun. Multi-task multi-agent reinforcement learning with task-entity transformers and value decomposition training. IEEE Transactions on Automation Science and Engineering, 2024

    2024

  103. [112]

    Multi-agent policy transfer via task relationship mod- eling

    Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Yipeng Kang, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. Multi-agent policy transfer via task relationship mod- eling. Science China Information Sciences, 2024

    2024

  104. [113]

    Relation-aware learning for multi-task multi-agent cooperative games.IEEE Trans- actions on Games, 2024

    Yang Yu, Likun Yang, Zhourui Guo, Yongjian Ren, Qiyue Yin, Junge Zhang, and Kaiqi Huang. Relation-aware learning for multi-task multi-agent cooperative games.IEEE Trans- actions on Games, 2024

    2024

  105. [114]

    Evolution- ary population curriculum for scaling multi-agent reinforcement learning

    Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu, and Xiaolong Wang. Evolution- ary population curriculum for scaling multi-agent reinforcement learning. InInternational Conference on Learning Representations, 2020

    2020

  106. [115]

    Towards skilled population curriculum for multi-agent reinforcement learning.arXiv preprint arXiv:2302.03429, 2023

    Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, and Changjie Fan. Towards skilled population curriculum for multi-agent reinforcement learning.arXiv preprint arXiv:2302.03429, 2023

    2023 arXiv

  107. [116]

    Variational automatic curriculum learning for sparse-reward coopera- tive multi-agent problems.Advances in Neural Information Processing Systems, 34:9681–9693, 2021

    Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, and Yi Wu. Variational automatic curriculum learning for sparse-reward coopera- tive multi-agent problems.Advances in Neural Information Processing Systems, 34:9681–9693, 2021. 31

    2021

  108. [117]

    Accelerate multi-agent reinforcement learning in zero-sum games with subgame curriculum learning

    Jiayu Chen, Zelai Xu, Yunfei Li, Chao Yu, Jiaming Song, Huazhong Yang, Fei Fang, Yu Wang, and Yi Wu. Accelerate multi-agent reinforcement learning in zero-sum games with subgame curriculum learning. In Proceedings of the AAAI Conference on Artificial Intelligence, vol- ume 38,...

    2024

  109. [118]

    Portal: Automatic curricula generation for multiagent reinforcement learning

    Jizhou Wu, Jianye Hao, Tianpei Yang, Xiaotian Hao, Yan Zheng, Weixun Wang, and Matthew E Taylor. Portal: Automatic curricula generation for multiagent reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 15934–15942, 2024

    2024

  110. [119]

    From few to more: Large-scale dynamic multiagent curriculum learning

    Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, and Yang Gao. From few to more: Large-scale dynamic multiagent curriculum learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 7293–7300, 2020

    2020

  111. [120]

    Automatic curriculum learning for large-scale cooperative multiagent systems

    Tianle Zhang, Zhen Liu, Zhiqiang Pu, and Jianqiang Yi. Automatic curriculum learning for large-scale cooperative multiagent systems. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3):912–930, 2022

    2022

  112. [121]

    Multiagent continual coordination via progressive task contextualization.IEEE Transactions on Neural Networks and Learning Systems, 2024

    Lei Yuan, Lihe Li, Ziqian Zhang, Fuxiang Zhang, Cong Guan, and Yang Yu. Multiagent continual coordination via progressive task contextualization.IEEE Transactions on Neural Networks and Learning Systems, 2024

    2024

  113. [122]

    Prioritized tasks mining for multi- task cooperative multi-agent reinforcement learning

    Yang Yu, Qiyue Yin, Junge Zhang, and Kaiqi Huang. Prioritized tasks mining for multi- task cooperative multi-agent reinforcement learning. InProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

    2023

  114. [123]

    Crmrl: Collaborative relationship meta reinforcement learning for effectively adapting to type changes in multi-robotic system.IEEE Robotics and Automation Letters, 2022

    Hongda Jia, Yong Zhao, Yuanzhao Zhai, Bo Ding, Huaimin Wang, and Qingtong Wu. Crmrl: Collaborative relationship meta reinforcement learning for effectively adapting to type changes in multi-robotic system.IEEE Robotics and Automation Letters, 2022

    2022

  115. [124]

    Cooperative multi-agent transfer learning with coali- tion pattern decomposition.IEEE Transactions on Games, 2023

    Tianze Zhou, Fubiao Zhang, Kun Shao, Zipeng Dai, Kai Li, Wenhan Huang, Weixun Wang, Bin Wang, Dong Li, Wulong Liu, et al. Cooperative multi-agent transfer learning with coali- tion pattern decomposition.IEEE Transactions on Games, 2023

    2023

  116. [125]

    other-play

    Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster. “other-play” for zero-shot coordination. In International Conference on Machine Learning, pages 4399–4410. PMLR, 2020

    2020

  117. [126]

    Trajectory diversity for zero- shot coordination

    Andrei Lupu, Brandon Cui, Hengyuan Hu, and Jakob Foerster. Trajectory diversity for zero- shot coordination. InInternational conference on machine learning, pages 7204–7213. PMLR, 2021

    2021

  118. [127]

    Any-play: An intrinsic augmentation for zero-shot coor- dination

    Keane Lucas and Ross E Allen. Any-play: An intrinsic augmentation for zero-shot coor- dination. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pages 853–861, 2022

    2022

  119. [128]

    Cooper- ative open-ended learning framework for zero-shot coordination

    Yang Li, Shao Zhang, Jichen Sun, Yali Du, Ying Wen, Xinbing Wang, and Wei Pan. Cooper- ative open-ended learning framework for zero-shot coordination. InInternational Conference on Machine Learning, pages 20470–20484. PMLR, 2023. 32

    2023

  120. [129]

    On the utility of learning about humans for human-ai coordination.Advances in neural information processing systems, 32, 2019

    Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, and Anca Dragan. On the utility of learning about humans for human-ai coordination.Advances in neural information processing systems, 32, 2019

    2019

  121. [130]

    Bench- marking multi-agent deep reinforcement learning algorithms in cooperative tasks

    Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V Albrecht. Bench- marking multi-agent deep reinforcement learning algorithms in cooperative tasks. InThirty- fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021

    2021

  122. [131]

    Multi-agent pathfinding: Definitions, vari- ants, and benchmarks

    RoniStern, NathanSturtevant, ArielFelner, SvenKoenig, HangMa, ThayneWalker, Jiaoyang Li, Dor Atzmon, Liron Cohen, TK Kumar, et al. Multi-agent pathfinding: Definitions, vari- ants, and benchmarks. In Proceedings of the International Symposium on Combinatorial Search, 2019

    2019

  123. [132]

    Context-aware sparse deep coordination graphs

    Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, and Chongjie Zhang. Context-aware sparse deep coordination graphs. In International Conference on Learning Representations, 2022

    2022

  124. [133]

    The dynamics of reinforcement learning in cooperative multiagent systems.AAAI/IAAI, 1998(746-752):2, 1998

    Caroline Claus and Craig Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems.AAAI/IAAI, 1998(746-752):2, 1998

    1998

  125. [134]

    o (marl\

    Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D Gaina, and Daniel Ionita. The multi-agent reinforcement learning in malm\" o (marl\" o) competition.arXiv preprint arXiv:1901.08129, 2019

    1901 arXiv

  126. [135]

    Emergent tool use from multi-agent autocurricula

    Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch. Emergent tool use from multi-agent autocurricula. InInternational conference on learning representations, 2019

    2019

  127. [136]

    Learning nearly de- composable value functions via communication minimization

    Tonghan Wang, Jianhao Wang, Chongyi Zheng, and Chongjie Zhang. Learning nearly de- composable value functions via communication minimization. InInternational Conference on Learning Representations, 2020

    2020

  128. [137]

    Towards human-level bimanual dexterous manipulation with reinforcement learning

    Yuanpei Chen, Yaodong Yang, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen Marcus McAleer, Hao Dong, and Song-Chun Zhu. Towards human-level bimanual dexterous manipulation with reinforcement learning. InThirty-sixth Conference on Neural Informatio...

    2022

  129. [138]

    Sumo (simulation of urban mobility)-an open-source traffic simulation

    Daniel Krajzewicz, Georg Hertkorn, Christian Rössel, and Peter Wagner. Sumo (simulation of urban mobility)-an open-source traffic simulation. InProceedings of the 4th middle East Symposium on Simulation and Modelling (MESM20002), pages 183–187, 2002

    2002

  130. [139]

    Sustaindc: Benchmarking for sustainable data center control.Ad- vances in Neural Information Processing Systems, 37:100630–100669, 2024

    Avisek Naug, Antonio Guillen-Perez, Ricardo Luna Gutierrez, Vineet Gundecha, Cullen Bash, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Dejan Markovikj, Lekhapriya Dheeraj Kashyap, et al. Sustaindc: Benchmarking for sustainable data center control.Ad- vances in Neural...

    2024

  131. [140]

    Wfcrl: A multi-agent reinforcement learning benchmark for wind farm control.arXiv preprint arXiv:2501.13592, 2025

    Claire Bizon Monroc, Ana Bušić, Donatien Dubuc, and Jiamin Zhu. Wfcrl: A multi-agent reinforcement learning benchmark for wind farm control.arXiv preprint arXiv:2501.13592, 2025. 33

    2025 arXiv

  132. [141]

    Marbler: An open platform for standardized evaluation of multi-robot reinforcement learn- ing algorithms

    Reza J Torbati, Shubham Lohiya, Shivika Singh, Meher S Nigam, and Harish Ravichandar. Marbler: An open platform for standardized evaluation of multi-robot reinforcement learn- ing algorithms. In 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), pages 5...

    2023

  133. [142]

    Multi-agent dynamic algorithm configuration

    Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, and Yang Yu. Multi-agent dynamic algorithm configuration. Advances in Neural Information Processing Systems, 35:20147–20161, 2022

    2022

  134. [143]

    Flatland-rl: Multi-agent reinforcement learning on trains

    Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider, Christian Scheller, Nil- abha Bhattacharya, Jeremy Watson, Adrian Egli, Christian Eichenberger, Christian Baum- berger, et al. Flatland-rl: Multi-agent reinforcement learning on trains. arXiv preprint arXiv:2012....

    2012 arXiv

  135. [144]

    Light aircraft game: A lightweight, scalable, gym- wrapped aircraft competitive environment with baseline reinforcement learning algorithms

    Qihan Liu, Yuhua Jiang, and Xiaoteng Ma. Light aircraft game: A lightweight, scalable, gym- wrapped aircraft competitive environment with baseline reinforcement learning algorithms. https://github.com/liuqh16/CloseAirCombat, 2022

    2022

  136. [145]

    A versatile multi-agent reinforcement learning benchmark for inventory management.arXiv preprint arXiv:2306.07542, 2023

    Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, and Jiang Bian. A versatile multi-agent reinforcement learning benchmark for inventory management.arXiv preprint arXiv:2306.07542, 2023

    2023 arXiv

  137. [146]

    Welfare diplomacy: Benchmarking language model cooperation

    Gabriel Mukobi, Hannah Erlebach, Niklas Lauffer, Lewis Hammond, Alan Chan, and Jesse Clifton. Welfare diplomacy: Benchmarking language model cooperation. arXiv preprint arXiv:2310.08901, 2023

    2023 arXiv

  138. [147]

    Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents.arXiv preprint arXiv:2308.10848, 2023

    WeizeChen, YushengSu, JingweiZuo, ChengYang, ChenfeiYuan, ChenQian, Chi-MinChan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents.arXiv preprint arXiv:2308.10848, 2023

    2023 arXiv

  139. [148]

    Collab-overcooked: Benchmarking and evaluating large language models as collaborative agents.arXiv preprint arXiv:2502.20073, 2025

    Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, and Xiaojie Wang. Collab-overcooked: Benchmarking and evaluating large language models as collaborative agents.arXiv preprint arXiv:2502.20073, 2025

    2025

  140. [149]

    Llmarena: Assessing capabilities of large language models in dynamic multi-agent environments

    Junzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, and Lijie Wen. Llmarena: Assessing capabilities of large language models in dynamic multi-agent environments. arXiv preprint arXiv:2402.16499, 2024

    2024 arXiv

  141. [150]

    Battleagentbench: A bench- mark for evaluating cooperation and competition capabilities of language models in multi- agent systems.arXiv preprint arXiv:2408.15971, 2024

    Wei Wang, Dan Zhang, Tao Feng, Boyan Wang, and Jie Tang. Battleagentbench: A bench- mark for evaluating cooperation and competition capabilities of language models in multi- agent systems.arXiv preprint arXiv:2408.15971, 2024

    2024 arXiv

  142. [151]

    Gobigger: A scalable platform for cooperative- competitive multi-agent interactive simulation

    Ming Zhang, Shenghan Zhang, Zhenjie Yang, Lekai Chen, Jinliang Zheng, Chao Yang, Chum- ing Li, Hang Zhou, Yazhe Niu, and Yu Liu. Gobigger: A scalable platform for cooperative- competitive multi-agent interactive simulation. InThe Eleventh International Conference on Learning R...

    2023

  143. [152]

    Pommerman: A multi-agent playground.arXiv preprint arXiv:1809.07124, 2018

    Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna. Pommerman: A multi-agent playground.arXiv preprint arXiv:1809.07124, 2018. 34

    2018 arXiv

  144. [153]

    Cooperative multi-agent control using deep reinforcement learning

    Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer. Cooperative multi-agent control using deep reinforcement learning. InInternational Conference on Autonomous Agents and Multiagent Systems, pages 66–83. Springer, 2017

    2017

  145. [154]

    Multi- agent reinforcement learning in sequential social dilemmas

    Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel. Multi- agent reinforcement learning in sequential social dilemmas. InInternational Conference on Autonomous Agents and Multiagent Systems, 2017

    2017

  146. [155]

    Concentration net- work for reinforcement learning of large-scale multi-agent systems

    Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, and Shiguang Wu. Concentration net- work for reinforcement learning of large-scale multi-agent systems. InProceedings of the AAAI Conference on Artificial Intelligence, 2022

    2022

  147. [156]

    Mate: Benchmarking multi-agent reinforcement learning in distributed target coverage con- trol

    Xuehai Pan, Mickel Liu, Fangwei Zhong, Yaodong Yang, Song-Chun Zhu, and Yizhou Wang. Mate: Benchmarking multi-agent reinforcement learning in distributed target coverage con- trol. Advances in Neural Information Processing Systems, 35:27862–27879, 2022

    2022

  148. [157]

    Magic: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration.arXiv preprint arXiv:2311.08562, 2023

    Lin Xu, Zhiyuan Hu, Daquan Zhou, Hongyu Ren, Zhen Dong, Kurt Keutzer, See Kiong Ng, and Jiashi Feng. Magic: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration.arXiv preprint arXiv:2311.08562, 2023

    2023 arXiv

  149. [158]

    Avalonbench: Evaluating llms playing the game of avalon.URL https://arxiv

    Jonathan Light, Min Cai, Sheng Shen, and Ziniu Hu. Avalonbench: Evaluating llms playing the game of avalon.URL https://arxiv. org/abs/2310.05036, 2023

    2023 arXiv

  150. [159]

    Villageragent: A graph- based multi-agent framework for coordinating complex task dependencies in minecraft.arXiv preprint arXiv:2406.05720, 2024

    Yubo Dong, Xukun Zhu, Zhengzhe Pan, Linchao Zhu, and Yi Yang. Villageragent: A graph- based multi-agent framework for coordinating complex task dependencies in minecraft.arXiv preprint arXiv:2406.05720, 2024

    2024 arXiv

  151. [160]

    Pokerbench: Training large language models to become professional poker players

    Richard Zhuang, Akshat Gupta, Richard Yang, Aniket Rahane, Zhengyu Li, and Gopala Anumanchipalli. Pokerbench: Training large language models to become professional poker players. arXiv preprint arXiv:2501.08328, 2025

    2025 arXiv

  152. [161]

    Multiagentbench: Evaluating the collaboration and competition of llm agents.arXiv preprint arXiv:2503.01935, 2025

    Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhen- hailong Wang, Cheng Qian, Xiangru Tang, Heng Ji, et al. Multiagentbench: Evaluating the collaboration and competition of llm agents.arXiv preprint arXiv:2503.01935, 2025

    2025 arXiv

  153. [162]

    Hybrid rl: Using both offline and online data can make rl efficient

    Yuda Song, Yifei Zhou, Ayush Sekhari, J Andrew Bagnell, Akshay Krishnamurthy, and Wen Sun. Hybrid rl: Using both offline and online data can make rl efficient. arXiv preprint arXiv:2210.06718, 2022

    2022 arXiv

  154. [163]

    Policy finetuning: Bridg- ing sample-efficient offline and online reinforcement learning.Advances in neural information processing systems, 34:27395–27407, 2021

    Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, and Yu Bai. Policy finetuning: Bridg- ing sample-efficient offline and online reinforcement learning.Advances in neural information processing systems, 34:27395–27407, 2021

    2021

  155. [164]

    Efficient online reinforcement learning with offline data

    Philip J Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine. Efficient online reinforcement learning with offline data. InInternational Conference on Machine Learning, pages 1577–1594. PMLR, 2023

    2023

  156. [165]

    SMACv2: An improved benchmark for cooperative multi-agent reinforcement learning

    Benjamin Ellis, Jonathan Cook, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Ma- hajan, Jakob Nicolaus Foerster, and Shimon Whiteson. SMACv2: An improved benchmark for cooperative multi-agent reinforcement learning. InThirty-seventh Conference on Neural Information Proc...

    2023

  157. [166]

    Offline multi-agent reinforce- ment learning with implicit global-to-local value regularization.Advances in Neural Informa- tion Processing Systems, 36:52413–52429, 2023

    Xiangsen Wang, Haoran Xu, Yinan Zheng, and Xianyuan Zhan. Offline multi-agent reinforce- ment learning with implicit global-to-local value regularization.Advances in Neural Informa- tion Processing Systems, 36:52413–52429, 2023

    2023

  158. [167]

    Counterfactual conservative q learning for offline multi-agent reinforcement learning

    Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, and Xiangyang Ji. Counterfactual conservative q learning for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 36:77290–77312, 2023

    2023

  159. [168]

    The hanabi challenge: A new frontier for ai research.Artificial Intelligence, 280:103216, 2020

    Nolan Bard, Jakob N Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, et al. The hanabi challenge: A new frontier for ai research.Artificial Intelligence, 280:103216, 2020

    2020

  160. [169]

    Machine theory of mind

    Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick. Machine theory of mind. InInternational conference on machine learning, pages 4218–4227. PMLR, 2018. 36

    2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.