According to the social intelligence hypothesis, cooperation is considered a key component of intelligence and is required to solve a wide range of problems, from everyday challenges like scheduling meetings to global challenges like mitigating climate change and providing humanitarian aid. Extending the ability for artificial intelligence (AI) to cooperate well is critical as AI becomes more prevalent in our lives. In recent years, multi-agent reinforcement learning (MARL) has emerged as a powerful approach to model and analyse the problem of cooperation among artificial agents. In this paper, we investigate the impact of communication on cooperation among reinforcement learning agents in social dilemmas. These are settings in which the short-term individual interests are in conflict with the longterm collective ones, thus each individual profits from defecting, but the overall group would benefit if everyone cooperates. We particularly focus on a temporally and spatially extended Stag-Hunt-like social dilemma that models animal foraging behaviour using principles from Optimal Foraging Theory. We propose a method for communication that combines a graph-based attention mechanism with deep reinforcement learning methods. Additionally, we examine several facets of communication, including the effects of the communication topology and the communication range. We find that greater cooperative behaviour can be achieved through graph-based communication using reinforcement learning in social dilemmas. Additionally, we find that during foraging, local communication promotes better cooperation than long-distance communication. Finally, we visualise and investigate the learned attention weights and explain how agents process communications from other agents.
Azmani, H , Rosseau, A , Nowe, A & Radulescu, R 2023, ' Cooperative Foraging Behaviour Through Multi-Agent Reinforcement Learning with Graph-Based Communication ', Paper presented at The 16th European Workshop on Reinforcement Learning, Brussels, Belgium, 14/09/23 - 16/09/23 . < https://openreview.net/pdf?id=epzrEgNNSWL >
Azmani, H. , Rosseau, A. , Nowe, A. , & Radulescu, R. (Accepted/In press). Cooperative Foraging Behaviour Through Multi-Agent Reinforcement Learning with Graph-Based Communication . Paper presented at The 16th European Workshop on Reinforcement Learning, Brussels, Belgium. https://openreview.net/pdf?id=epzrEgNNSWL
@conference{84c2c965887148989f6d4dce3bc9f0b8,
title = " Cooperative Foraging Behaviour Through Multi-Agent Reinforcement Learning with Graph-Based Communication " ,
abstract = " According to the social intelligence hypothesis, cooperation is considered a keycomponent of intelligence and is required to solve a wide range of problems, fromeveryday challenges like scheduling meetings to global challenges like mitigatingclimate change and providing humanitarian aid. Extending the ability for artificialintelligence (AI) to cooperate well is critical as AI becomes more prevalent in ourlives. In recent years, multi-agent reinforcement learning (MARL) has emergedas a powerful approach to model and analyse the problem of cooperation amongartificial agents. In this paper, we investigate the impact of communication oncooperation among reinforcement learning agents in social dilemmas. These aresettings in which the short-term individual interests are in conflict with the longterm collective ones, thus each individual profits from defecting, but the overallgroup would benefit if everyone cooperates. We particularly focus on a temporally and spatially extended Stag-Hunt-like social dilemma that models animalforaging behaviour using principles from Optimal Foraging Theory. We proposea method for communication that combines a graph-based attention mechanismwith deep reinforcement learning methods. Additionally, we examine severalfacets of communication, including the effects of the communication topologyand the communication range. We find that greater cooperative behaviour canbe achieved through graph-based communication using reinforcement learning insocial dilemmas. Additionally, we find that during foraging, local communicationpromotes better cooperation than long-distance communication. Finally, we visualise and investigate the learned attention weights and explain how agents processcommunications from other agents. " ,
author = " Hicham Azmani and Andries Rosseau and Ann Nowe and Roxana Radulescu " ,
year = " 2023 " ,
month = aug,
language = " English " ,
note = " The 16th European Workshop on Reinforcement Learning, EWRL 2023 Conference date: 14-09-2023 Through 16-09-2023 " ,
url = " https://ewrl.wordpress.com/ewrl16-2023/ " ,
}