mlsanityData to 14 Oct 2026

Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning

arXiv 2606.14693 ↗First posted 12 Jun 2026cs.MA cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts arise not only across objectives but also across agents with different observations, roles, and contributions. We propose Preference Coordinated Multi-agent Policy Optimization (PCMA), which learns coordinated agent-specific preferences to enable complementary trade-offs among agents. Theoretically, we formulate cooperative MOMARL as a team-optimal equilibrium problem, and show that, under suitable conditions, preference diversity yields a first-order improvement in the team objective. Experiments on multiple cooperative MOMA environments and a practical traffic-control scenario show that PCMA improves both performance and trade-off coordination.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 12 Jun 2026.

Builds on

18 of 34 references.

References · 18

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  6. 2019The StarCraft Multi-Agent Challengecited by 597
  7. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
  8. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  9. 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 242
  10. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  11. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 177
  12. 2018Dynamic Weights in Multi-Objective Deep Reinforcement Learningcited by 100
6 more
  1. 2022PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithmcited by 58
  2. 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
  3. 2019Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Surveycited by 47
  4. 2014On the Characterization of Local Nash Equilibria in Continuous Gamescited by 22
  5. 2026MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learningcited by 8
  6. 2024MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learningcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Multi-Agent Reinforcement Learning via Agent-Specific Preference
  2. 2024Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
  3. 2025MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
  4. 2025Preference-based Multi-Objective Reinforcement Learning
  5. 2025Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
  6. 2024Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
  7. 2023Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
  8. 2024Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning