Online Learning of Temporal Dependencies for Sustainable Foraging Problem

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Bibliographic Details
Main Authors:	Payne, John, Aishwaryaprajna, Lewis, Peter R.
Format:	Preprint
Published:	2024
Subjects:	Multiagent Systems Machine Learning Neural and Evolutionary Computing
Online Access:	Acceder al recurso
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author	Payne, John Aishwaryaprajna Lewis, Peter R.
author_facet	Payne, John Aishwaryaprajna Lewis, Peter R.
contents	The sustainable foraging problem is a dynamic environment testbed for exploring the forms of agent cognition in dealing with social dilemmas in a multi-agent setting. The agents need to resist the temptation of individual rewards through foraging and choose the collective long-term goal of sustainability. We investigate methods of online learning in Neuro-Evolution and Deep Recurrent Q-Networks to enable agents to attempt the problem one-shot as is often required by wicked social problems. We further explore if learning temporal dependencies with Long Short-Term Memory may be able to aid the agents in developing sustainable foraging strategies in the long term. It was found that the integration of Long Short-Term Memory assisted agents in developing sustainable strategies for a single agent, however failed to assist agents in managing the social dilemma that arises in the multi-agent scenario.
format	Preprint
id	arxiv_https___arxiv_org_abs_2407_01501
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Online Learning of Temporal Dependencies for Sustainable Foraging Problem Payne, John Aishwaryaprajna Lewis, Peter R. Multiagent Systems Machine Learning Neural and Evolutionary Computing The sustainable foraging problem is a dynamic environment testbed for exploring the forms of agent cognition in dealing with social dilemmas in a multi-agent setting. The agents need to resist the temptation of individual rewards through foraging and choose the collective long-term goal of sustainability. We investigate methods of online learning in Neuro-Evolution and Deep Recurrent Q-Networks to enable agents to attempt the problem one-shot as is often required by wicked social problems. We further explore if learning temporal dependencies with Long Short-Term Memory may be able to aid the agents in developing sustainable foraging strategies in the long term. It was found that the integration of Long Short-Term Memory assisted agents in developing sustainable strategies for a single agent, however failed to assist agents in managing the social dilemma that arises in the multi-agent scenario.
title	Online Learning of Temporal Dependencies for Sustainable Foraging Problem
topic	Multiagent Systems Machine Learning Neural and Evolutionary Computing
url	https://arxiv.org/abs/2407.01501

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