mlsanityData to 7 Oct 2026

The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning

arXiv 2401.10949 ↗First posted 18 Jan 2024cs.MA cs.LG cs.SYcited by 5

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Abstract (arXiv)

This paper explores the integration of optimal transport (OT) theory with multi-agent reinforcement learning (MARL). This integration uses OT to handle distributions and transportation problems to enhance the efficiency, coordination, and adaptability of MARL. There are five key areas where OT can impact MARL: (1) policy alignment, where OT's Wasserstein metric is used to align divergent agent strategies towards unified goals; (2) distributed resource management, employing OT to optimize resource allocation among agents; (3) addressing non-stationarity, using OT to adapt to dynamic environmental shifts; (4) scalable multi-agent learning, harnessing OT for decomposing large-scale learning objectives into manageable tasks; and (5) enhancing energy efficiency, applying OT principles to develop sustainable MARL systems. This paper articulates how the synergy between OT and MARL can address scalability issues, optimize resource distribution, align agent policies in cooperative environments, and ensure adaptability in dynamically changing conditions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 50 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

20 of 50 references.

References · 20

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  3. 2015Optimal Transport for Domain Adaptationcited by 457
  4. 2020Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptationcited by 86
  5. 2018Optimal Transport for Multi-source Domain Adaptation under Target Shiftcited by 67
  6. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  7. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  8. 2019Hierarchical Optimal Transport for Multimodal Distribution Alignmentcited by 38
  9. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  10. 2021Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approachcited by 27
  11. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  12. 2022A Critical Review of Communications in Multi-Robot Systemscited by 16
8 more
  1. 2021On the complexity of the optimal transport problem with graph-structured costcited by 14
  2. 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structurecited by 14
  3. 2022Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learningcited by 13
  4. 2022Scalable multi-agent reinforcement learning for distributed control of residential energy flexibilitycited by 11
  5. 2019Entropic regularization of continuous optimal transport problemscited by 10
  6. 2023Risk-Aware Reinforcement Learning through Optimal Transport Theorycited by 7
  7. 2022Scalable Model-based Policy Optimization for Decentralized Networked Systemscited by 6
  8. 2021Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systemscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learningcited by 4
  2. 2024Optimal Transport-Assisted Risk-Sensitive Q-Learningcited by 3
  3. 2025Wasserstein Barycenter Soft Actor-Criticcited by 1
  4. 2026Federated Distributional Reinforcement Learning with Distributional Critic Regularizationcited by 1
  5. 2026Learning to Orchestrate Agents under Uncertaintycited by 1