19 lines
2.0 KiB
Markdown
19 lines
2.0 KiB
Markdown
---
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layout: single
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title: "Aquarium"
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categories: research MARL reinforcement-learning multi-agent
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excerpt: "Exploring Predator-Prey Dynamics in multi-agent reinforcement-learning"
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header:
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teaser: assets/figures/20_aquarium.png
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---
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{:style="display:block; width:40%" .align-right}
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Recent advances in multi-agent reinforcement learning have enabled the modeling of complex interactions between agents in simulated environments. In particular, predator-prey dynamics have garnered significant interest, and various simulations have been adapted to meet unique requirements. To avoid further time-intensive development efforts, we introduce *Aquarium*, a versatile multi-agent reinforcement learning environment designed for studying predator-prey interactions and emergent behavior. *Aquarium* is open-source and seamlessly integrates with the PettingZoo framework, allowing for a quick start using established algorithm implementations. It features physics-based agent movement on a two-dimensional, edge-wrapping plane. Both the agent-environment interactions (observations, actions, rewards) and environmental parameters (agent speed, prey reproduction, predator starvation, and more) are fully customizable. In addition to providing a resource-efficient visualization, *Aquarium* supports video recording, facilitating a visual understanding of agent behavior.
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To showcase the environment's capabilities, we conducted preliminary studies using proximal policy optimization (PPO) to train multiple prey agents to evade a predator. Consistent with existing literature, we found that individual learning leads to worse performance, while parameter sharing significantly improves coordination and sample efficiency.
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{% cite kolle2024aquarium %}
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{:style="display:block; width:70%" .align-center}
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{:style="display:block; width:70%" .align-center}
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