29 lines
1.8 KiB
Markdown
29 lines
1.8 KiB
Markdown
---
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layout: single
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title: "AI-Fusion: Emergence Detection for Mixed MARL Systems."
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categories: acoustic anomaly-detection projects
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excerpt: "Bringing together agents can be an inherent safety problem. Building the basis to mix and match."
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header:
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teaser: assets/images/projects/robot.png
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---
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{: .align-left style="padding:0.1em; width:5em"}
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In cooperation with [Fraunhofer IKS](https://www.iks.fraunhofer.de/), this project explored emergent effects in multi-agent reinforcement learning scenarios, such as mixed-vendor autonomous systems. Emergence, defined as complex dynamics arising from interactions among entities and their environment, was a key focus.
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{: .align-center style="padding:0.1em; width:80%"}
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<div class="table-right" style="text-align:right">
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| {: style="margin:0em; padding:0em; width:15em"} |
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| [GitHub Repo](https://github.com/illiumst/marl-factory-grid/) |
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| [Install via PyPI](https://pypi.org/project/Marl-Factory-Grid/) |
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| [Read-the-docs](https://marl-factory-grid.readthedocs.io/en/latest/) |
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| Read the Paper (TBA) |
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</div>
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We developed a high-performance environment in Python, adhering to the [gymnasium](https://gymnasium.farama.org/main/) specifications, to facilitate reinforcement learning algorithm training.
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This environment uniquely supports a variety of scenarios through `modules` and `configurations`, with capabilities for per-agent observations and handling of multi-agent and sequential actions.
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Additionally, a [Unity demonstrator unit](https://github.com/illiumst/F-IKS_demonstrator) was developed to replay and analyze specific scenarios, aiding in the investigation of emerging dynamics.
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