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Over the past decade, neural network (NN)-based controllers have demonstratedremarkable efficacy in a variety of decision-making tasks. However, theirblack-box nature and the risk of unexpected behaviors and surprising resultspose a challenge to their deployment in real-world systems with strongguarantees of correctness and safety. We address these limitations byinvestigating the transformation of NN-based controllers into equivalent softdecision tree (SDT)-based controllers and its impact on verifiability.Differently from previous approaches, we focus on discrete-output NNcontrollers including rectified linear unit (ReLU) activation functions as wellas argmax operations. We then devise an exact but cost-effective transformationalgorithm, in that it can automatically prune redundant branches. We evaluateour approach using two benchmarks from the OpenAI Gym environment. Our resultsindicate that the SDT transformation can benefit formal verification, showingruntime improvements of up to 21x and 2x for MountainCar-v0 and CartPole-v0,respectively.

Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers