Objective-aware Traffic Simulation via Inverse Reinforcement Learning
  Traffic simulators act as an essential component in the operating and
planning of transportation systems. Conventional traffic simulators usually
employ a calibrated physical car-following model to describe vehicles'
behaviors and their interactions with traffic environment. However, there is no
universal physical model that can accurately predict the pattern of vehicle's
behaviors in different situations. A fixed physical model tends to be less
effective in a complicated environment given the non-stationary nature of
traffic dynamics. In this paper, we formulate traffic simulation as an inverse
reinforcement learning problem, and propose a parameter sharing adversarial
inverse reinforcement learning model for dynamics-robust simulation learning.
Our proposed model is able to imitate a vehicle's trajectories in the real
world while simultaneously recovering the reward function that reveals the
vehicle's true objective which is invariant to different dynamics. Extensive
experiments on synthetic and real-world datasets show the superior performance
of our approach compared to state-of-the-art methods and its robustness to
variant dynamics of traffic.
