logo 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.

Relation emergence

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.