mlsanityData to 14 Oct 2026

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

arXiv 1703.06182 ↗First posted 17 Mar 2017cs.LG cs.AI cs.MAcited by 148

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Abstract (arXiv)

Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.

Key facts. Cited by 148 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Mar 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 35; 2022: 36; 2023: 23; 2024: 17; 2025: 24; 2026: 12.

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  1. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 81
  2. 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
  3. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 56
  4. 2024An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learningcited by 50
  5. 2022Towards a Standardised Performance Evaluation Protocol for Cooperative MARLcited by 49
  6. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 36
  7. 2021Offline Decentralized Multi-Agent Reinforcement Learningcited by 33
  8. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 33
  9. 2021Continuous Coordination As a Realistic Scenario for Lifelong Learningcited by 32
  10. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 32
  11. 2021Social Coordination and Altruism in Autonomous Drivingcited by 28
  12. 2021Decentralized Local Stochastic Extra-Gradient for Variational Inequalitiescited by 25
38 more
  1. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 23
  2. 2022Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimizationcited by 20
  3. 2023RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorizationcited by 20
  4. 2021Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agentscited by 19
  5. 2022A Survey on Reinforcement Learning in Aviation Applicationscited by 19
  6. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  7. 2021MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelscited by 16
  8. 2021Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Trainingcited by 16
  9. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 16
  10. 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
  11. 2021Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guaranteescited by 15
  12. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Trafficcited by 15
  13. 2021Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selectioncited by 14
  14. 2022Optimal Algorithms for Decentralized Stochastic Variational Inequalitiescited by 14
  15. 2023First Order Methods with Markovian Noise: from Acceleration to Variational Inequalitiescited by 14
  16. 2023Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learningcited by 13
  17. 2021Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systemscited by 12
  18. 2022Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learningcited by 12
  19. 2022Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimizationcited by 12
  20. 2021Spatial Intention Maps for Multi-Agent Mobile Manipulationcited by 11
  21. 2021Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Criticcited by 11
  22. 2023From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARLcited by 11
  23. 2021Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledgecited by 10
  24. 2022Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Surveycited by 10
  25. 2023Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiencescited by 10
  26. 2024On Centralized Critics in Multi-Agent Reinforcement Learningcited by 10
  27. 2022A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learningcited by 9
  28. 2023Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrationscited by 9
  29. 2021Last-Iterate Convergence of Saddle-Point Optimizers via High-Resolution Differential Equationscited by 8
  30. 2022Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomizationcited by 8
  31. 2019Independent Generative Adversarial Self-Imitation Learning in Cooperative Multiagent Systemscited by 7
  32. 2021Direct-Search for a Class of Stochastic Min-Max Problemscited by 7
  33. 2023Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalitiescited by 7
  34. 2021Learning Meta Representations for Agents in Multi-Agent Reinforcement Learningcited by 6
  35. 2021Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guaranteescited by 6
  36. 2022Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functionscited by 6
  37. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
  38. 2023Heterogeneous Embodied Multi-Agent Collaborationcited by 6

50 most cited of 148 All 148 (JSON)