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Authors: Michael Kölle 1 ; Yannick Erpelding 1 ; Fabian Ritz 1 ; Thomy Phan 2 ; Steffen Illium 1 and Claudia Linnhoff-Popien 1

Affiliations: 1 Institute of Informatics, LMU Munich, Munich, Germany ; 2 Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, U.S.A.

Keyword(s): Reinforcement Learning, Multi-Agent Systems, Predator-Prey.

Abstract: Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-prey dynamics have captured substantial interest and various simulations been tailored to unique requirements. To prevent further time-intensive developments, we introduce Aquarium, a comprehensive Multi-Agent Reinforcement Learning environment for predator-prey interaction, enabling the study of emergent behavior. Aquarium is open source and offers a seamless integration of the PettingZoo framework, allowing a quick start with proven algorithm implementations. It features physics-based agent movement on a two-dimensional, edge-wrapping plane. The agent-environment interaction (observations, actions, rewards) and the environment settings (agent speed, prey reproduction, predator starvation, and others) are fully customizable. Besides a resource-efficient visualization, Aquarium supports to record video files, providing a visual comprehension of agent behavior. To demonstrate the environment’s capabilities, we conduct preliminary studies which use PPO to train multiple prey agents to evade a predator. In accordance to the literature, we find Individual Learning to result in worse performance than Parameter Sharing, which significantly improves coordination and sample-efficiency. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kölle, M.; Erpelding, Y.; Ritz, F.; Phan, T.; Illium, S. and Linnhoff-Popien, C. (2024). Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics Through Multi-Agent Reinforcement Learning Algorithms. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 59-70. DOI: 10.5220/0012382300003636

@conference{icaart24,
author={Michael Kölle. and Yannick Erpelding. and Fabian Ritz. and Thomy Phan. and Steffen Illium. and Claudia Linnhoff{-}Popien.},
title={Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics Through Multi-Agent Reinforcement Learning Algorithms},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={59-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012382300003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics Through Multi-Agent Reinforcement Learning Algorithms
SN - 978-989-758-680-4
IS - 2184-433X
AU - Kölle, M.
AU - Erpelding, Y.
AU - Ritz, F.
AU - Phan, T.
AU - Illium, S.
AU - Linnhoff-Popien, C.
PY - 2024
SP - 59
EP - 70
DO - 10.5220/0012382300003636
PB - SciTePress