  The lack of trust in algorithms is usually an issue when using Reinforcement
Learning (RL) agents for control in real-world domains such as production
plants, autonomous vehicles, or traffic-related infrastructure, partly due to
the lack of verifiability of the model itself. In such scenarios, Petri nets
(PNs) are often available for flowcharts or process steps, as they are
versatile and standardized. In order to facilitate integration of RL models and
as a step towards increasing AI trustworthiness, we propose an approach that
uses PNs with three main advantages over typical RL approaches: Firstly, the
agent can now easily be modeled with a combined state including both external
environmental observations and agent-specific state information from a given
PN. Secondly, we can enforce constraints for state-dependent actions through
the inherent PN model. And lastly, we can increase trustworthiness by verifying
PN properties through techniques such as model checking. We test our approach
on a typical four-way intersection traffic light control setting and present
our results, beating cycle-based baselines.
