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_posts/projects/2020-05-01-FIKS.md
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![logo](\assets\images\projects\robot.png){: .align-left style="padding:0.1em; width:5em"}
In cooperation with [Fraunhofer IKS](https://www.iks.fraunhofer.de/) this project aimed to investigate and detect emergent effects in multi-agent reinforcement learning scenarios, i.e., mixed-vendor autonomous systems (AI fusion).
Emergence in general refers to emerging dynamics of higher complexity (i.e., sum), which are fed by interacting entities (each other and the environment) of a specific complexity level (regarding their policies and capabilities).
![Relation emergence](/assets/images/projects/rel_emergence.png){: .align-center style="padding:0.1em; width:30em"}
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.
In this context, we developed a full-stack, high-performance environment in Python, following the [gymnasium](https://gymnasium.farama.org/main/) specification for the training of reinforcement learning algorithms.
![Relation emergence](/assets/images/projects/rel_emergence.png){: .align-center style="padding:0.1em; width:80%"}
<div class="table-right">
![logo](\assets\images\projects\full_domain.png){: .align-right style="padding:0.5em; width:10em"}
| [GitHub Repo](https://github.com/illiumst/marl-factory-grid/) | [Read-the-docs](https://marl-factory-grid.readthedocs.io/en/latest/)  |
| [Install via PyPI](https://pypi.org/project/Marl-Factory-Grid/) | Read the Paper (TBA)  |
<div class="table-right" style="text-align:right">
| ![logo](\assets\images\projects\full_domain.png){: style="margin:0em; padding:0em; width:15em"} |
| [GitHub Repo](https://github.com/illiumst/marl-factory-grid/) |
| [Install via PyPI](https://pypi.org/project/Marl-Factory-Grid/) |
| [Read-the-docs](https://marl-factory-grid.readthedocs.io/en/latest/) |
| Read the Paper (TBA) |
</div>
The main differentiation from already established MARL environments is its ability to employ various scenarios as a combination of `modules` and `configurations`. As well as the option to define per-agent observations, including placeholder and combined observation slices (grid-world). Moreover, this environment can handle multi-agent scenarios as well as sequential actions for inter-step observations.
We developed a high-performance environment in Python, adhering to the [gymnasium](https://gymnasium.farama.org/main/) specifications, to facilitate reinforcement learning algorithm training.
Furthermore, we designed and implemented a [Unity demonstrator unit](https://github.com/illiumst/F-IKS_demonstrator) that can load and replay specific pre-recorded scenarios. This way, emerging unwanted and unsafe situations can be replayed and intuitively investigated.
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.
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.