Bottom-up Reputation Promotes Cooperation With Multi-agent Reinforcement Learning

Abstract

Reputation serves as a powerful mechanism for promoting cooperation in multi-agent systems, as agents are more inclined to cooperate with those of good social standing. While existing multi-agent reinforcement learning methods typically rely on predefined social norms to assign reputations, the question of how a population reaches a consensus on judgement when agents hold private, independent views remains unresolved. In this paper, we propose a novel bottom-up reputation learning method, Learning with Reputation Reward (LR2), designed to promote cooperative behaviour through rewards shaping based on assigned reputation. Our agent architecture includes a dilemma policy that determines cooperation by considering the impact on neighbours, and an evaluation policy that assigns reputations to affect the actions of neighbours while optimizing self-objectives. It operates using local observations and interaction-based rewards, without relying on centralized modules or predefined norms. Our f

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyren2025bottom

Related papers

• Cooperation And Reputation Dynamics With Reinforcement Learning (2021)3.58
• Modeling Human Reputation-seeking Behavior In A Spatio-temporally Complex Public Good Provision Game (2025)0.00
• Social Influence As Intrinsic Motivation For Multi-agent Deep Reinforcement Learning (2018)0.00
• Deception In Social Learning: A Multi-agent Reinforcement Learning Perspective (2021)0.00
• Learning Through Probing: A Decentralized Reinforcement Learning Architecture For Social Dilemmas (2018)0.00
• Multi-agent Cooperation Through Learning-aware Policy Gradients (2024)0.00
• Reciprocal Reward Influence Encourages Cooperation From Self-interested Agents (2024)1.91
• Trust-based Consensus In Multi-agent Reinforcement Learning Systems (2022)0.00