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| single | AI-Fusion: Emergence Detection for Mixed MARL Systems. | acoustic anomaly-detection projects | Bringing together agents can be an inherent safety problem. Building the basis to mix and match. |
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In cooperation with Fraunhofer IKS, 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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We developed a high-performance environment in Python, adhering to the gymnasium specifications, to facilitate reinforcement learning algorithm training.
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 was developed to replay and analyze specific scenarios, aiding in the investigation of emerging dynamics.