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metadata
title: README
emoji: 🌍
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colorTo: red
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HIVEX
Multi-Agent Reinforcement Learning environment suite with a focus on critical ecological challenges.
About
The motivation of the hivex suite is to provide advanced reinforcement learning benchmarking environments with an emphasis on: (1) real-world scenarios, (2) multi-agent systems, (3) investigating problems and solutions with high impact on society.
Train & Test Scripts: https://github.com/hivex-research/hivex
Available Environments: https://github.com/hivex-research/hivex-environments
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| Wind Farm Control | Wildfire Resource Management | Drone-Based Reforestation | Ocean Plastic Collection | Aerial WildFire Suppression |
WFC |
WRM |
DBR |
OPC |
AWS |
| Task Count / Patterns | Task Count / Terrain Elevation Levels | Task Count / Terrain Elevation Levels | Task Count / Terrain Elevation Levels | Task Count / Terrain Elevation Levels |
2 / 9 |
3 / 10 |
7 / 10 |
4 / - |
9 / 10 |
The paper preprint titled "HIVEX: A High-Impact Environment Suite for Multi-Agent Research (extended version)" introducing the HIVEX environments can be found here: https://arxiv.org/abs/2501.04180