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.

Aquarium 是一个开源的、全面的多智能体强化学习环境，旨在研究捕食者 - 被捕食者交互中的自发行为。该环境使用 PettingZoo 框架和 PPO 算法实现了资源高效的可视化，并证实了参数共享在提高协调性和样本效率方面优于个体学习。