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Multi-Agent Determinantal Q-Learning

arXiv 2006.01482 ↗First posted 2 Jun 2020cs.LG cs.MAcited by 36

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Abstract (arXiv)

Centralized training with decentralized execution has become an important paradigm in multi-agent learning. Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution. In this paper, we eliminate this restriction by proposing multi-agent determinantal Q-learning. Our method is established on Q-DPP, an extension of determinantal point process (DPP) with partition-matroid constraint to multi-agent setting. Q-DPP promotes agents to acquire diverse behavioral models; this allows a natural factorization of the joint Q-functions with no need for a priori structural constraints on the value function or special network architectures. We demonstrate that Q-DPP generalizes major solutions including VDN, QMIX, and QTRAN on decentralizable cooperative tasks. To efficiently draw samples from Q-DPP, we adopt an existing sample-by-projection sampler with theoretical approximation guarantee. The sampler also benefits exploration by coordinating agents to cover orthogonal directions in the state space during multi-agent training. We evaluate our algorithm on various cooperative benchmarks; its effectiveness has been demonstrated when compared with the state-of-the-art.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2020.

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Citing papers per year: 2021: 15; 2022: 12; 2023: 3; 2024: 0; 2025: 3; 2026: 3.

36 papers.

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  1. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 180
  2. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 153
  3. 2023Heterogeneous-Agent Reinforcement Learningcited by 67
  4. 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
  5. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  6. 2021UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformerscited by 39
  7. 2021Modelling Behavioural Diversity for Learning in Open-Ended Gamescited by 39
  8. 2021Multi-Agent Constrained Policy Optimisationcited by 32
  9. 2021Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Taskscited by 32
  10. 2021Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approachcited by 27
  11. 2021Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systemscited by 24
  12. 2025Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignmentcited by 17
24 more
  1. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  2. 2021Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Gamescited by 14
  3. 2021Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignmentcited by 12
  4. 2021Lyapunov Exponents for Diversity in Differentiable Gamescited by 8
  5. 2023Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learningcited by 8
  6. 2021A Game-Theoretic Approach to Multi-Agent Trust Region Optimizationcited by 7
  7. 2022On-the-fly Strategy Adaptation for ad-hoc Agent Coordinationcited by 7
  8. 2022A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Processcited by 7
  9. 2021Diversity-based Trajectory and Goal Selection with Hindsight Experience Replaycited by 6
  10. 2022Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learningcited by 6
  11. 2022Learning to Transfer Role Assignment Across Team Sizescited by 5
  12. 2022Multi-agent Actor-Critic with Time Dynamical Opponent Modelcited by 3
  13. 2022Hardness of Maximum Likelihood Learning of DPPscited by 3
  14. 2021Neural Auto-Curriculacited by 2
  15. 2022Scalable Sampling for Nonsymmetric Determinantal Point Processescited by 2
  16. 2022Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processescited by 2
  17. 2022Taming Multi-Agent Reinforcement Learning with Estimator Variance Reductioncited by 1
  18. 2023Diverse Policy Optimization for Structured Action Spacecited by 1
  19. 2025Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Designcited by 1
  20. 2025AOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisionscited by 1
  21. 2022RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learningcited by 0
  22. 2026Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learningcited by 0
  23. 2026Adaptive TD-Lambda for Cooperative Multi-agent Reinforcement Learningcited by 0
  24. 2026HySTAR: Anchored Hypergraphs for Stable Credit Assignment in Cooperative Multi-Agent Reinforcement Learningcited by 0