Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning

TL;DR

This paper introduces Theory of Mind Agents with Guilt Aversion (ToMAGA), which incorporate social awareness into reinforcement learning, leading to improved cooperation in social dilemmas like the Stag Hunt game.

Contribution

The paper develops a novel affective reinforcement learning framework that integrates Theory of Mind and guilt aversion, enhancing cooperative behavior in complex social environments.

Findings

ToMAGA agents learn cooperative strategies in Stag Hunt.

Guilt aversion as reward shaping improves social dilemma outcomes.

Standard RL agents often fail to learn optimal cooperation.

Abstract

Guilt aversion induces experience of a utility loss in people if they believe they have disappointed others, and this promotes cooperative behaviour in human. In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM). We aim to build a new kind of affective reinforcement learning agents, called Theory of Mind Agents with Guilt Aversion (ToMAGA), which are equipped with an ability to think about the wellbeing of others instead of just self-interest. To validate the agent design, we use a general-sum game known as Stag Hunt as a test bed. As standard reinforcement learning agents could learn suboptimal policies in social dilemmas like Stag Hunt, we propose to use belief-based guilt aversion as a reward shaping mechanism. We show that our belief-based guilt averse agents can efficiently learn cooperative behaviours in Stag Hunt Games.