mlsanityData to 14 Oct 2026

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

arXiv 1810.02912 ↗First posted 5 Oct 2018cs.LG cs.AI cs.MAcited by 261

Not on the map. Why?

Abstract (arXiv)

Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, as well as settings that do not provide global states, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems.

Key facts. Cited by 261 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Oct 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 58; 2022: 54; 2023: 44; 2024: 38; 2025: 40; 2026: 26.

261 papers.

Sort:

By area:

  1. 2021V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RLcited by 114
  2. 2021Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectificationcited by 61
  3. 2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 58
  4. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 56
  5. 2024An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learningcited by 50
  6. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 49
  7. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  8. 2021Shapley Counterfactual Credits for Multi-Agent Reinforcement Learningcited by 38
  9. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  10. 2022Deep Transformer Q-Networks for Partially Observable Reinforcement Learningcited by 36
  11. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  12. 2021Offline Decentralized Multi-Agent Reinforcement Learningcited by 33
38 more
  1. 2021RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agentscited by 31
  2. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 30
  3. 2021Deep Interactive Bayesian Reinforcement Learning via Meta-Learningcited by 29
  4. 2023A Review of Cooperation in Multi-agent Learningcited by 29
  5. 2021SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learningcited by 26
  6. 2022Scalable Multi-Agent Model-Based Reinforcement Learningcited by 25
  7. 2021Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rolloutscited by 24
  8. 2021MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learningcited by 23
  9. 2022PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaborationcited by 23
  10. 2022What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?cited by 22
  11. 2021Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learningcited by 20
  12. 2021Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Spacecited by 20
  13. 2023MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimizationcited by 20
  14. 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorizationcited by 20
  15. 2021Regularized Softmax Deep Multi-Agent Q-Learningcited by 19
  16. 2021Learning Efficient Multi-Agent Cooperative Visual Explorationcited by 19
  17. 2021HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanismcited by 19
  18. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  19. 2021Collaborative Visual Navigationcited by 18
  20. 2022Influencing Long-Term Behavior in Multiagent Reinforcement Learningcited by 18
  21. 2022Asynchronous Actor-Critic for Multi-Agent Reinforcement Learningcited by 18
  22. 2022Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systemscited by 18
  23. 2023Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?cited by 18
  24. 2021MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelscited by 16
  25. 2021Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Trainingcited by 16
  26. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 16
  27. 2022RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on statescited by 16
  28. 2021Divergence-Regularized Multi-Agent Actor-Criticcited by 15
  29. 2024Generative Emergent Communication: Large Language Model is a Collective World Modelcited by 15
  30. 2022Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computationcited by 14
  31. 2023Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learningcited by 14
  32. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarioscited by 13
  33. 2022MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learningcited by 12
  34. 2021Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learningcited by 11
  35. 2022Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantizationcited by 11
  36. 2022ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Rewardcited by 11
  37. 2022PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learningcited by 11
  38. 2022Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecificationcited by 11

50 most cited of 261 All 261 (JSON)