Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic

This paper tackles the problem of learning generalizable congestionmitigation strategies in simple representations of traffic. In particular, we look to mixed-autonomy ring roads as depictions of instabilities common to many generic settings, and ask the question: What features are needed to ensure that policies here can be adapted to typical multi-lane highways? To answer this, we study the implications of the scale of the source task and the modeling of pseudo-lane change events within it on the transferability of policies learned to complex networks. Our findings suggest that negating the effects of boundary conditions and introducing lane changes that approximately match trends in more complex systems can significantly improve the generalizability of learned behaviors.