# Mediated Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2306.08419 · HTML: https://mlsanity.com/paper/2306.08419/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 59 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 30 of 34)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018)
- [Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow](https://mlsanity.com/paper/1810.00821/) (2018)
- [Deep Coordination Graphs](https://mlsanity.com/paper/1910.00091/) (2019)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [A multi-agent reinforcement learning model of common-pool resource appropriation](https://mlsanity.com/paper/1707.06600/) (2017)
- [Learning to Incentivize Other Learning Agents](https://mlsanity.com/paper/2006.06051/) (2020)
- [Maintaining cooperation in complex social dilemmas using deep reinforcement learning](https://mlsanity.com/paper/1707.01068/) (2017)
- [Prosocial learning agents solve generalized Stag Hunts better than selfish ones](https://mlsanity.com/paper/1709.02865/) (2017)
- [Learning Fairness in Multi-Agent Systems](https://mlsanity.com/paper/1910.14472/) (2019)
- [Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.09421/) (2020)
- [COLA: Consistent Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/2203.04098/) (2022)
- [CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1809.05188/) (2018)
- [Evolving intrinsic motivations for altruistic behavior](https://mlsanity.com/paper/1811.05931/) (2018)
- [The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2008.02616/) (2020)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [Learning Reciprocity in Complex Sequential Social Dilemmas](https://mlsanity.com/paper/1903.08082/) (2019)
- [Consequentialist conditional cooperation in social dilemmas with imperfect information](https://mlsanity.com/paper/1710.06975/) (2017)
- [Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences](https://mlsanity.com/paper/2011.05373/) (2020)
- [Proximal Learning With Opponent-Learning Awareness](https://mlsanity.com/paper/2210.10125/) (2022)
- [Emergent Communication under Competition](https://mlsanity.com/paper/2101.10276/) (2021)
- [Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming](https://mlsanity.com/paper/2201.08484/) (2022)
- [Multi-Receiver Online Bayesian Persuasion](https://mlsanity.com/paper/2106.06480/) (2021)
- [Adaptive Mechanism Design: Learning to Promote Cooperation](https://mlsanity.com/paper/1806.04067/) (2018)
- [Information Design in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.06807/) (2023)

## Built on by (most cited first, 5 of 5)

- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Cooperative AI via Decentralized Commitment Devices](https://mlsanity.com/paper/2311.07815/) (2023)
- [Learning to Negotiate via Voluntary Commitment](https://mlsanity.com/paper/2503.03866/) (2025)
- [Guaranteed Trust Region Optimization via Two-Phase KL Penalization](https://mlsanity.com/paper/2312.05405/) (2023)
- [Learning Recommender Mechanisms for Bayesian Stochastic Games](https://mlsanity.com/paper/2505.22979/) (2025)

## Abstract (arXiv)

The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private information. This results in agents that forgo their individual goals in favour of social good, which can potentially be exploited by selfish defectors. We argue that cooperation also requires agents' identities and boundaries to be respected by making sure that the emergent behaviour is an equilibrium, i.e., a convention that no agent can deviate from and receive higher individual payoffs. Inspired by advances in mechanism design, we propose to solve the problem of cooperation, defined as finding socially beneficial equilibrium, by using mediators. A mediator is a benevolent entity that may act on behalf of agents, but only for the agents that agree to it. We show how a mediator can be trained alongside agents with policy gradient to maximize social welfare subject to constraints that encourage agents to cooperate through the mediator. Our experiments in matrix and iterative games highlight the potential power of applying mediators in MARL.