Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner’s Dilemma

Grace Feehan, Shaheen Fatima

2022

Abstract

Reinforcement learning algorithms applied to social dilemmas sometimes struggle with converging to mutual cooperation against like-minded partners, particularly when utilising greedy behavioural selection methods. Recent research has demonstrated how affective cognitive mechanisms, such as mood and emotion, might facilitate increased rates of mutual cooperation when integrated with these algorithms. This research has, thus far, primarily utilised mobile multi-agent frameworks to demonstrate this relationship - where they have also identified interaction structure as a key determinant of the emergence of cooperation. Here, we use a deterministic, static interaction structure to provide deeper insight into how a particular moody reinforcement learner might encourage the evolution of cooperation in the Iterated Prisoner’s Dilemma. In a novel grid environment, we both replicated original test parameters and then varied the distribution of agents and the payoff matrix. We found that behavioural trends from past research were present (with suppressed magnitude), and that the proportion of mutual cooperations was heightened when both the influence of mood and the cooperation index of the payoff matrix chosen increased. Changing the proportion of moody agents in the environment only increased mutual cooperations by virtue of introducing cooperative agents to each other.

Paper Citation

in Harvard Style

Feehan G. and Fatima S. (2022). Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner’s Dilemma. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 146-157. DOI: 10.5220/0010787500003116

in Bibtex Style

@conference{icaart22,
author={Grace Feehan and Shaheen Fatima},
title={Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner’s Dilemma},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={146-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010787500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner’s Dilemma
SN - 978-989-758-547-0
AU - Feehan G.
AU - Fatima S.
PY - 2022
SP - 146
EP - 157
DO - 10.5220/0010787500003116