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Most reinforcement learning approaches used in behavior generation utilizevectorial information as input. However, this requires the network to have apre-defined input-size -- in semantic environments this means assuming themaximum number of vehicles. Additionally, this vectorial representation is notinvariant to the order and number of vehicles. To mitigate the above-stateddisadvantages, we propose combining graph neural networks with actor-criticreinforcement learning. As graph neural networks apply the same network toevery vehicle and aggregate incoming edge information, they are invariant tothe number and order of vehicles. This makes them ideal candidates to be usedas networks in semantic environments -- environments consisting of objectslists. Graph neural networks exhibit some other advantages that make themfavorable to be used in semantic environments. The relational information isexplicitly given and does not have to be inferred. Moreover, graph neuralnetworks propagate information through the network and can gather higher-degreeinformation. We demonstrate our approach using a highway lane-change scenarioand compare the performance of graph neural networks to conventional ones. Weshow that graph neural networks are capable of handling scenarios with avarying number and order of vehicles during training and application.

Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments