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Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications

arXiv 1812.11794 ↗First posted 31 Dec 2018cs.LG cs.AI cs.MAcited by 127

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Abstract (arXiv)

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This paper addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multi-agent deep RL (MADRL) is presented, including non-stationarity, partial observability, continuous state and action spaces, multi-agent training schemes, multi-agent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed, with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to future development of more robust and highly useful multi-agent learning methods for solving real-world problems.

Key facts. Cited by 127 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Dec 2018.

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Citing papers per year: 2020: 1; 2021: 31; 2022: 12; 2023: 21; 2024: 27; 2025: 26; 2026: 9.

127 papers.

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  10. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  11. 2021Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligencecited by 27
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38 more
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  9. 2021The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learningcited by 15
  10. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
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  12. 2023Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principlescited by 12
  13. 2023Transformers in Reinforcement Learning: A Surveycited by 12
  14. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systemscited by 11
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  16. 2024On Limitation of Transformer for Learning HMMscited by 10
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  23. 2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
  24. 2021Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learningcited by 7
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  26. 2023Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learningcited by 7
  27. 2024SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systemscited by 7
  28. 2021MDPGT: Momentum-based Decentralized Policy Gradient Trackingcited by 6
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  30. 2024Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaborationcited by 6
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  32. 2022Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learningcited by 5
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  36. 2021Two-stage training algorithm for AI robot soccercited by 4
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  38. 2023DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learningcited by 4

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