Learning Reciprocity in Complex Sequential Social Dilemmas

by   Tom Eccles, et al.

Reciprocity is an important feature of human social interaction and underpins our cooperative nature. What is more, simple forms of reciprocity have proved remarkably resilient in matrix game social dilemmas. Most famously, the tit-for-tat strategy performs very well in tournaments of Prisoner's Dilemma. Unfortunately this strategy is not readily applicable to the real world, in which options to cooperate or defect are temporally and spatially extended. Here, we present a general online reinforcement learning algorithm that displays reciprocal behavior towards its co-players. We show that it can induce pro-social outcomes for the wider group when learning alongside selfish agents, both in a 2-player Markov game, and in 5-player intertemporal social dilemmas. We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.


page 4

page 5

page 8


Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Matrix games like Prisoner's Dilemma have guided research on social dile...

Translucent Players: Explaining Cooperative Behavior in Social Dilemmas

In the last few decades, numerous experiments have shown that humans do ...

Incorporating Rivalry in Reinforcement Learning for a Competitive Game

Recent advances in reinforcement learning with social agents have allowe...

An Evolutionary Game Model for Understanding Fraud in Consumption Taxes

This paper presents a computational evolutionary game model to study and...

Trust-ya: design of a multiplayer game for the study of small group processes

This paper presents the design of a cooperative multi-player betting gam...

Graph Neural Networks to Predict Sports Outcomes

Predicting outcomes in sports is important for teams, leagues, bettors, ...

Please sign up or login with your details

Forgot password? Click here to reset