mlsanityData to 7 Oct 2026

R-MADDPG for Partially Observable Environments and Limited Communication

arXiv 2002.06684 ↗First posted 16 Feb 2020cs.MA cs.AIcited by 36

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Abstract (arXiv)

There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The real world has challenging conditions for multiagent learning systems, such as its partial observable and nonstationary nature. Moreover, if agents must share a limited resource (e.g. network bandwidth) they must all learn how to coordinate resource use. This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable set-tings and limited communication. We investigate recurrency effects on performance and communication use of a team of agents. We demonstrate that the resulting framework learns time dependencies for sharing missing observations, handling resource limitations, and developing different communication patterns among agents.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2020.

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Citing papers per year: 2021: 14; 2022: 7; 2023: 4; 2024: 4; 2025: 3; 2026: 4.

36 papers.

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  1. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  2. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  3. 2021Unbiased Asymmetric Reinforcement Learning under Partial Observabilitycited by 24
  4. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 23
  5. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  6. 2022Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Realcited by 16
  7. 2021Network-wide traffic signal control optimization using a multi-agent deep reinforcement learningcited by 12
  8. 2023From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARLcited by 11
  9. 2023Informed POMDP: Leveraging Additional Information in Model-Based RLcited by 11
  10. 2022A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learningcited by 9
  11. 2021SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learningcited by 7
  12. 2024On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flowcited by 5
24 more
  1. 2026Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamicscited by 5
  2. 2024Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learningcited by 4
  3. 2021Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transportcited by 3
  4. 2021Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learningcited by 3
  5. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  6. 2022Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learningcited by 2
  7. 2023Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learningcited by 2
  8. 2021Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environmentscited by 1
  9. 2021MACRPO: Multi-Agent Cooperative Recurrent Policy Optimizationcited by 1
  10. 2021Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially Observable Environmentscited by 1
  11. 2022A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learningcited by 1
  12. 2022Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learningcited by 1
  13. 2024Towards Fault Tolerance in Multi-Agent Reinforcement Learningcited by 1
  14. 2025Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observabilitycited by 1
  15. 2021Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channelscited by 0
  16. 2021Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learningcited by 0
  17. 2022A New Approach to Training Multiple Cooperative Agents for Autonomous Drivingcited by 0
  18. 2022Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learningcited by 0
  19. 2023On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environmentcited by 0
  20. 2025Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithmcited by 0
  21. 2025Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspectivecited by 0
  22. 2026Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learningcited by 0
  23. 2026Value-Aware Prediction for Robust Multi-Agent Coordination Under Communication Losscited by 0
  24. 2026Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidancecited by 0