Tackling Asymmetric and Circular Sequential Social Dilemmas with Reinforcement Learning and Graph-based Tit-for-Tat

TL;DR

This paper introduces Circular Sequential Social Dilemmas (CSSD) to model asymmetric cooperation and proposes a graph-based Tit-for-Tat strategy combined with reinforcement learning to promote cooperation among self-interested agents.

Contribution

It extends existing social dilemma frameworks to include circular and asymmetric cooperation and develops a novel graph-based TFT method for multi-agent reinforcement learning.

Findings

Graph-based TFT encourages mutual cooperation in circular dilemmas.

The approach effectively handles asymmetric cooperation scenarios.

Experiments show improved cooperation among agents in a grid world environment.

Abstract

In many societal and industrial interactions, participants generally prefer their pure self-interest at the expense of the global welfare. Known as social dilemmas, this category of non-cooperative games offers situations where multiple actors should all cooperate to achieve the best outcome but greed and fear lead to a worst self-interested issue. Recently, the emergence of Deep Reinforcement Learning (RL) has generated revived interest in social dilemmas with the introduction of Sequential Social Dilemma (SSD). Cooperative agents mixing RL policies and Tit-for-tat (TFT) strategies have successfully addressed some non-optimal Nash equilibrium issues. However, this kind of paradigm requires symmetrical and direct cooperation between actors, conditions that are not met when mutual cooperation become asymmetric and is possible only with at least a third actor in a circular way. To tackle this issue, this paper extends SSD with Circular Sequential Social Dilemma (CSSD), a new kind of Markov games that better generalizes the diversity of cooperation between agents. Secondly, to address such circular and asymmetric cooperation, we propose a candidate solution based on RL policies and a graph-based TFT. We conducted some experiments on a simple multi-player grid world which offers adaptable cooperation structures. Our work confirmed that our graph-based approach is beneficial to address circular situations by encouraging self-interested agents to reach mutual cooperation.