  Most reinforcement learning approaches used in behavior generation utilize
vectorial information as input. However, this requires the network to have a
pre-defined input-size -- in semantic environments this means assuming the
maximum number of vehicles. Additionally, this vectorial representation is not
invariant to the order and number of vehicles. To mitigate the above-stated
disadvantages, we propose combining graph neural networks with actor-critic
reinforcement learning. As graph neural networks apply the same network to
every vehicle and aggregate incoming edge information, they are invariant to
the number and order of vehicles. This makes them ideal candidates to be used
as networks in semantic environments -- environments consisting of objects
lists. Graph neural networks exhibit some other advantages that make them
favorable to be used in semantic environments. The relational information is
explicitly given and does not have to be inferred. Moreover, graph neural
networks propagate information through the network and can gather higher-degree
information. We demonstrate our approach using a highway lane-change scenario
and compare the performance of graph neural networks to conventional ones. We
show that graph neural networks are capable of handling scenarios with a
varying number and order of vehicles during training and application.
