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Data-driven simulators promise high data-efficiency for driving policylearning. When used for modelling interactions, this data-efficiency becomes abottleneck: Small underlying datasets often lack interesting and challengingedge cases for learning interactive driving. We address this challenge byproposing a simulation method that uses in-painted ado vehicles for learningrobust driving policies. Thus, our approach can be used to learn policies thatinvolve multi-agent interactions and allows for training via state-of-the-artpolicy learning methods. We evaluate the approach for learning standardinteraction scenarios in driving. In extensive experiments, our workdemonstrates that the resulting policies can be directly transferred to afull-scale autonomous vehicle without making use of any traditional sim-to-realtransfer techniques such as domain randomization.

Learning Interactive Driving Policies via Data-driven Simulation