mlsanityData to 14 Oct 2026

Learning Adaptive Safety for Multi-Agent Systems

arXiv 2309.10657 ↗First posted 19 Sep 2023cs.RO cs.LG cs.MAcited by 3

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Abstract (arXiv)

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy and flexibility of ASRL, and assess generalization and scalability to out-of-distribution scenarios. Code and supplementary material are public online.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

31 of 48 references.

References · 31

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  5. 2017Constrained Policy Optimizationcited by 983
  6. 2017Safe Reinforcement Learning via Shieldingcited by 449
  7. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  8. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
  9. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  10. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  11. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  12. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 151
19 more
  1. 2019IPO: Interior-point Policy Optimization under Constraintscited by 132
  2. 2023OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Researchcited by 80
  3. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 74
  4. 2022Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentationcited by 69
  5. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 68
  6. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 60
  7. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
  8. 2019Control Barrier Functions for Systems with High Relative Degreecited by 31
  9. 2019Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functionscited by 21
  10. 2020Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertaintiescited by 20
  11. 2022Robust Policy Optimization in Deep Reinforcement Learningcited by 15
  12. 2021Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Changecited by 9
  13. 2022Differentiable Safe Controller Design through Control Barrier Functionscited by 8
  14. 2021Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibilitycited by 7
  15. 2021Recursive Feasibility Guided Optimal Parameter Adaptation of Differential Convex Optimization Policies for Safety-Critical Systemscited by 7
  16. 2023Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specificationscited by 7
  17. 2022Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systemscited by 2
  18. 2022Compatibility checking of multiple control barrier functions for input constrained systemscited by 2
  19. 2023Consolidated Control Barrier Functions: Synthesis and Online Verification via Adaptation under Input Constraintscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systemscited by 5
  2. 2025Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardwarecited by 0
  3. 2026Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learningcited by 0