Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks

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.