# Emergent Prosociality in Multi-Agent Games Through Gifting

arXiv: https://arxiv.org/abs/2105.06593 · HTML: https://mlsanity.com/paper/2105.06593/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 17 of 17)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [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)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Inequity aversion improves cooperation in intertemporal social dilemmas](https://mlsanity.com/paper/1803.08884/) (2018)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Stable Opponent Shaping in Differentiable Games](https://mlsanity.com/paper/1811.08469/) (2018)
- [Prosocial learning agents solve generalized Stag Hunts better than selfish ones](https://mlsanity.com/paper/1709.02865/) (2017)
- [Theory of Minds: Understanding Behavior in Groups Through Inverse Planning](https://mlsanity.com/paper/1901.06085/) (2019)
- [Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.12127/) (2019)
- [On the Critical Role of Conventions in Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2104.02871/) (2021)
- [Learning How to Dynamically Route Autonomous Vehicles on Shared Roads](https://mlsanity.com/paper/1909.03664/) (2019)
- [Multi-Agent Safe Planning with Gaussian Processes](https://mlsanity.com/paper/2008.04452/) (2020)
- [Altruistic Autonomy: Beating Congestion on Shared Roads](https://mlsanity.com/paper/1810.11978/) (2018)
- [Learning Cooperative Games](https://mlsanity.com/paper/1505.00039/) (2015)

## Built on by (most cited first, 12 of 12)

- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Diverse Conventions for Human-AI Collaboration](https://mlsanity.com/paper/2310.15414/) (2023)
- [Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games](https://mlsanity.com/paper/2410.07863/) (2024)
- [The emergence of division of labor through decentralized social sanctioning](https://mlsanity.com/paper/2208.05568/) (2022)
- [Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperation](https://mlsanity.com/paper/2405.19328/) (2024)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.15695/) (2026)

## Abstract (arXiv)

Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game. However, state of the art reinforcement learning algorithms often suffer from converging to socially less desirable equilibria when multiple equilibria exist. Previous works address this challenge with explicit reward shaping, which requires the strong assumption that agents can be forced to be prosocial. We propose using a less restrictive peer-rewarding mechanism, gifting, that guides the agents toward more socially desirable equilibria while allowing agents to remain selfish and decentralized. Gifting allows each agent to give some of their reward to other agents. We employ a theoretical framework that captures the benefit of gifting in converging to the prosocial equilibrium by characterizing the equilibria's basins of attraction in a dynamical system. With gifting, we demonstrate increased convergence of high risk, general-sum coordination games to the prosocial equilibrium both via numerical analysis and experiments.