mlsanityData to 14 Oct 2026

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System

arXiv 2501.13727 ↗First posted 23 Jan 2025cs.MA cs.AIcited by 1

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Abstract (arXiv)

Safety and scalability are two critical challenges faced by practical Multi-Agent Systems (MAS). However, existing Multi-Agent Reinforcement Learning (MARL) algorithms that rely solely on reward shaping are ineffective in ensuring safety, and their scalability is rather limited due to the fixed-size network output. To address these issues, we propose a novel framework, Scalable Safe MARL (SS-MARL), to enhance the safety and scalability of MARL methods. Leveraging the inherent graph structure of MAS, we design a multi-layer message passing network to aggregate local observations and communications of varying sizes. Furthermore, we develop a constrained joint policy optimization method in the setting of local observation to improve safety. Simulation experiments demonstrate that SS-MARL achieves a better trade-off between optimality and safety compared to baselines, and its scalability significantly outperforms the latest methods in scenarios with a large number of agents.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

24 of 30 references.

References · 24

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  5. 2017Constrained Policy Optimizationcited by 983
  6. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  7. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 656
  8. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
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  11. 2016Learning Multiagent Communication with Backpropagationcited by 366
  12. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
12 more
  1. 2018Graph Convolutional Reinforcement Learningcited by 152
  2. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 104
  3. 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 93
  4. 2019Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Criticscited by 47
  5. 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
  6. 2019Learning Transferable Cooperative Behavior in Multi-Agent Teamscited by 44
  7. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 36
  8. 2021Attention-based Reinforcement Learning for Real-Time UAV Semantic Communicationcited by 14
  9. 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learningcited by 8
  10. 2021Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learningcited by 7
  11. 2023Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanismcited by 6
  12. 2021DRQN-based 3D Obstacle Avoidance with a Limited Field of Viewcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learningcited by 0

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