Who Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learning

arXiv:2610.07491 · cs.LG, cs.AI, cs.GT, cs.MA · Submitted 2026-10-05 · Read on arXiv

cs.LG, cs.AI, cs.GT, cs.MA

Submitted: 2026-10-05

Updated: 2026-10-05

Comments: 20 pages, 2 figures, 4 tables. Project page with code: https://lira-marl.github.io

Project page: https://lira-marl.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Melting Pot 2.0
• DeepMind Lab2D
• Generalized Inner Loop Meta-Learning
• Multi-Agent Constrained Policy Optimisation
• Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
• Generalized Nash Equilibrium Solutions in Dynamic Games With Shared Constraints
• Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
• Proximal Policy Optimization Algorithms
• Deep Incentive Design with Differentiable Equilibrium Blocks
• An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
• A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

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