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.
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