The Price Of Paranoia: Robust Risk-sensitive Cooperation In Non-stationary Multi-agent Reinforcement Learning

Abstract

Cooperative equilibria are fragile. When agents learn alongside each other rather than in a fixed environment, the process of learning destabilizes the cooperation they are trying to sustain: every gradient step an agent takes shifts the distribution of actions its partner will play, turning a cooperative partner into a source of stochastic noise precisely where the cooperation decision is most sensitive. We study how this co-learning noise propagates through the structure of coordination games, and find that the cooperative equilibrium, even when strongly Pareto-dominant, is exponentially unstable under standard risk-neutral learning, collapsing irreversibly once partner noise crosses the game's critical cooperation threshold. The natural response to apply distributional robustness to hedge against partner uncertainty makes things strictly worse: risk-averse return objectives penalize the high-variance cooperative action relative to defection, widening the instability region rather th

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyganguly2026the

Related papers

• Robust Cooperative Multi-agent Reinforcement Learning:a Mean-field Type Game Perspective (2024)0.00
• Intrinsic Fluctuations Of Reinforcement Learning Promote Cooperation (2022)9.23
• Training Generalizable Collaborative Agents Via Strategic Risk Aversion (2026)0.00
• Cooperation And Reputation Dynamics With Reinforcement Learning (2021)3.58
• Loss Aversion Fosters Coordination Among Independent Reinforcement Learners (2019)0.00
• Toward Risk-based Optimistic Exploration For Cooperative Multi-agent Reinforcement Learning (2023)0.00
• Taming Equilibrium Bias In Risk-sensitive Multi-agent Reinforcement Learning (2024)0.00
• Emergent Cooperation Under Uncertain Incentive Alignment (2024)2.26